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Widyaningsih, Yekti, i Rugun Ivana. "WEIBULL-POISSON DISTRIBUTION AND THEIR APPLICATION TO SYSTEMATIC PARALLEL RISK". BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, nr 1 (13.03.2024): 0029–42. http://dx.doi.org/10.30598/barekengvol18iss1pp0053-0064.
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The Weibull-Poisson distribution represents a continuous distribution type applicable to various forms of hazard, including monotone up, monotone down, and upside-down bathtub shapes that ascend. The distribution characterizes lifetimes and can effectively model failures within a series of systems, which evolves from the Exponential-Poisson distribution. This distribution emerges through the compounding of the Weibull Distribution and Zero Truncated Poisson Distribution. The compounding itself integrates several mathematical properties, such as statistical order and Taylor’s number expansion, to reach its final form. Alongside the formulation of the Weibull-Poisson distribution, this paper includes the probability density function, distribution function, rth moment, rth central moment, mean, and variance. For illustration, the Weibull-Poisson distribution is applied to guinea pig survival data after being infected with Turblece virus Bacilli.
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Nourinejhad Zarghani, Shaheen, Mehran Monavari, Amin Nourinejhad Zarghani, Sahar Nouri, Jens Ehlers, Joachim Hamacher, Martina Bandte i Carmen Büttner. "Quantifying Plant Viruses: Evolution from Bioassay to Infectivity Dilution Curves along the Model of Tobamoviruses". Viruses 16, nr 3 (12.03.2024): 440. http://dx.doi.org/10.3390/v16030440.
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This review describes the development of the bioassay as a means of quantifying plant viruses, with particular attention to tobamovirus. It delves into various models used to establish a correlation between virus particle concentration and the number of induced local lesions (the infectivity dilution curve), including the Poisson, Furumoto and Mickey, Kleczkowski, Growth curve, and modified Poisson models. The parameters of each model are described, and their application or performance in the context of the tobacco mosaic virus is explored. This overview highlights the enduring value of the infectivity dilution curve in tobamovirus quantification, providing valuable insights for researchers or practitioners of bioassays and theoreticians of modeling.
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Pakes, Anthony G., i A. C. Trajstman. "Some properties of continuous-state branching processes, with applications to Bartoszyński’s virus model". Advances in Applied Probability 17, nr 1 (marzec 1985): 23–41. http://dx.doi.org/10.2307/1427050.
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It is known that Bartoszyński’s model for the growth of rabies virus in an infected host is a continuous branching process. We show by explicit construction that any such process is a randomly time-transformed compound Poisson process having a negative linear drift.This connection is exploited to obtain limit theorems for the population size and for the jump times in the rabies model. Some of these results are obtained in a more general context wherein the compound Poisson process is replaced by a subordinator.
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Pakes, Anthony G., i A. C. Trajstman. "Some properties of continuous-state branching processes, with applications to Bartoszyński’s virus model". Advances in Applied Probability 17, nr 01 (marzec 1985): 23–41. http://dx.doi.org/10.1017/s0001867800014634.
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It is known that Bartoszyński’s model for the growth of rabies virus in an infected host is a continuous branching process. We show by explicit construction that any such process is a randomly time-transformed compound Poisson process having a negative linear drift. This connection is exploited to obtain limit theorems for the population size and for the jump times in the rabies model. Some of these results are obtained in a more general context wherein the compound Poisson process is replaced by a subordinator.
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Maneking, Faranika Deysi G., Deiby Tineke Salaki i Djoni Hatidja. "Model Regresi Poisson Tergeneralisasi untuk Anak Gizi Buruk di Sulawesi Utara". JURNAL ILMIAH SAINS 20, nr 2 (31.10.2020): 141. http://dx.doi.org/10.35799/jis.20.2.2020.29133.
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Salah satu masalah kesehatan yang sering terjadi di Indonesia adalah gizi buruk. Seorang anak yang mengalami gizi buruk akan rentan terhadap berbagai penyakit karena sistem kekebalan tubuhnya mudah terinfeksi virus. Jumlah kasus anak gizi buruk merupakan data diskrit yang dapat dimodelkan dengan regresi poisson. Penelitian ini bertujuan untuk menentukan model regresi poisson tergeneralisasi dalam mengatasi overdispersi pada model regresi poisson dari jumlah kasus gizi buruk dan menentukan faktor-faktor yang mempengaruhi jumlah kasus gizi buruk di Sulawesi Utara tahun 2018. Data yang digunakan berupa jumlah kasus gizi buruk sebagai variabel respon dan sejumlah variabel prediktor dengan unit pengamatannya adalah kota dan kabupaten di Sulawesi Utara. Hasil penelitian menunjukkan bahwa variabel yang mempengaruhi jumlah anak gizi buruk di Sulawesi Utara adalah persentase bayi dengan berat badan lahir rendah (BBLR) dan variabel yang tidak berpengaruh signifikan adalah jumlah posyandu, persentase keluarga yang memiliki sanitasi layak pakai, jumlah penduduk miskin, persentase bayi yang menggunakan ASI ekslusif dengan model regresi poisson tergeneralisasi.Kata kunci: Keluarga eksponensial; multikolinearitas; overdispersi; Sulawesi UtaraGeneralized Poisson Regression Model For Malnourished Children in North SulawesiABSTRACTOne of the health problems that often occurs in Indonesia is malnutrition. A child who is suffering from malnutrition will be vulnerable to various diseases because his immune system is easily infected by a virus. The number of cases of malnutrition children is discrete data that can be modeled by Poisson regression. This study aimed to determine the Generalized Poisson Regression (GPR) model for handling the overdispersion occurred in the Poisson regression model of the number of malnutrished children case and also to determine the factors that influence the number of malnutrished children case in North Sulawesi in 2018. This study utilizes the number of cases of malnutrition as a response variable and a number of predictor variables with the observation unit was cities and districts in North Sulawesi. The results of this study indicate that the variables that significantly affect the number of malnourished children in North Sulawesi include the percentage of child with low weight birth (LWB) and variables that have no significant effect are the number of posyandu, the percentage of families who have decent sanitation, the number of poor people, the percentage of babies who use exclusive breast milk with the generalized poisson regression model Keywords: Exponential family; multicolinearity; overdispersion; North SulawesiSalah satu masalah kesehatan yang sering terjadi di Indonesia adalah gizi buruk. Seorang anak yang mengalami gizi buruk akan rentan terhadap berbagai penyakit karena sistem kekebalan tubuhnya mudah terinfeksi virus. Jumlah kasus anak gizi buruk merupakan data diskrit yang dapat dimodelkan dengan regresi poisson. Penelitian ini bertujuan untuk menentukan model regresi poisson tergeneralisasi dalam mengatasi overdispersi pada model regresi poisson dari jumlah kasus gizi buruk dan menentukan faktor-faktor yang mempengaruhi jumlah kasus gizi buruk di Sulawesi Utara tahun 2018. Data yang digunakan berupa jumlah kasus gizi buruk sebagai variabel respon dan sejumlah variabel prediktor dengan unit pengamatannya adalah kota dan kabupaten di Sulawesi Utara. Hasil penelitian menunjukkan bahwa variabel yang mempengaruhi jumlah anak gizi buruk di Sulawesi Utara adalah persentase bayi dengan berat badan lahir rendah (BBLR) dan variabel yang tidak berpengaruh signifikan adalah jumlah posyandu, persentase keluarga yang memiliki sanitasi layak pakai, jumlah penduduk miskin, persentase bayi yang menggunakan ASI ekslusif dengan model regresi poisson tergeneralisasi .Kata kunci: Keluarga eksponensial; multikolinearitas; overdispersi; Sulawesi UtaraGeneralized Poisson Regression Model For Malnourished Children in North Sulawesi ABSTRACTOne of the health problems that often occurs in Indonesia is malnutrition. A child who is suffering from malnutrition will be vulnerable to various diseases because his immune system is easily infected by a virus. The number of cases of malnutrition children is discrete data that can be modeled by Poisson regression. This study aimed to determine the Generalized Poisson Regression (GPR) model for handling the overdispersion occurred in the Poisson regression model of the number of malnutrished children case and also to determine the factors that influence the number of malnutrished children case in North Sulawesi in 2018. This study utilizes the number of cases of malnutrition as a response variable and a number of predictor variables with the observation unit was cities and districts in North Sulawesi. The results of this study indicate that the variables that significantly affect the number of malnourished children in North Sulawesi include the percentage of child with low weight birth (LWB) and variables that have no significant effect are the number of posyandu, the percentage of families who have decent sanitation, the number of poor people, the percentage of babies who use exclusive breast milk with the generalized poisson regression model Keywords: Exponential family; multicolinearity; overdispersion; North Sulawesi
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Akbarzadeh Baghban, Alireza, Asma Pourhoseingholi, Farid Zayeri, Ali Akbar Jafari i Seyed Moayed Alavian. "Application of Zero-Inflated Poisson Mixed Models in Prognostic Factors of Hepatitis C". BioMed Research International 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/403151.
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Background and Objectives. In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C.Methods. The data was collected from a longitudinal study during 2005–2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared.Results. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors.Conclusions. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.
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Seghier, Fatma Zohra, Halim Zeghdoudi i Vinoth Raman. "A Novel Discrete Distribution: Properties and Application Using Nipah Virus Infection Data Set". European Journal of Statistics 3 (9.01.2022): 3. http://dx.doi.org/10.28924/ada/stat.3.3.
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In this study, the Poisson (PD) distribution was compounded with a continuous distribution to produce the Poisson XLindley distribution (PXLD). Its raw moments and central moments are acquired as a result of a general expression for its rth factorial moment concerning origin being derived. Additionally, the expressions for its coefficient of variation, skewness, kurtosis and index of dispersion have been provided. For the estimate of its parameters, in particular, the methods of maximum likelihood and moments have been addressed. The applicability of the proposed distribution in modeling real data sets on Nipah virus infection, number of Hemocytometer yeast cell count data, and epileptic seizure counts data is examined by analyzing two real-world data sets.
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Seghier, Fatma Zohra, Halim Zeghdoudi i Abbes Benchaabane. "A Size-Biased Poisson-Gamma Lindley Distribution with Application". European Journal of Statistics 1, nr 1 (22.10.2021): 132–47. http://dx.doi.org/10.28924/ada/stat.1.132.
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In this paper, a size-biased Poisson-Gamma Lindley distribution (SBPGLD) has been obtained by size-biasing the Poisson-Gamma Lindley distribution (PGLD) introduced recently by Nedjar and Zeghdoudi (2020). Some of its statistical properties have been discussed. The method of maximum likelihood and the method of moments for the estimation of its parameters have been discussed. Also, an application on the real data of survival times of (56) Indian state of Kerala individus infected with Nipah virus is given.
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Nourinejhad Zarghani, Shaheen, Mehran Monavari, Jens Ehlers, Joachim Hamacher, Carmen Büttner i Martina Bandte. "Comparison of Models for Quantification of Tomato Brown Rugose Fruit Virus Based on a Bioassay Using a Local Lesion Host". Plants 11, nr 24 (9.12.2022): 3443. http://dx.doi.org/10.3390/plants11243443.
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Considering the availability of serological and molecular biological methods, the bioassay has been paled into insignificance, although it is the only experimental method that can be used to demonstrate the infectivity of a virus. We compared goodness-of-fit and predictability power of five models for the quantification of tomato brown rugose fruit virus (ToBRFV) based on local lesion assays: the Kleczkowski model, Furumoto and Mickey models I and II, the Gokhale and Bald model (growth curve model), and the modified Poisson model. For this purpose, mechanical inoculations onto Nicotiana tabacum L. cv. Xanthi nc and N. glutionosa L. with defined virus concentrations were first performed with half-leaf randomization in a Latin square design. Subsequently, models were implemented using Python software and fitted to the number of local lesions. All models could fit to the data for quantifying ToBRFV based on local lesions, among which the modified Poisson model had the best prediction of virus concentration in spike samples based on local lesions, although data of individual indicator plants showed variations. More accurate modeling was obtained from the test plant N. glutinosa than from N. tabacum cv. Xanthi nc. The position of the half-leaves on the test plants had no significant effect on the number of local lesions.
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Zuhrat, Lily, Dodi Devianto i Izzati Rahmi HG. "Pemodelan Jumlah Kasus DBD Yang Meninggal Di Kota Padang Dengan Menggunakan Regresi Poisson". Jurnal Matematika UNAND 4, nr 4 (26.07.2019): 57. http://dx.doi.org/10.25077/jmu.4.4.57-64.2015.
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Demam Berdarah Dengue (DBD) adalah penyakit infeksi yang disebabkan oleh virus Dengue melalui gigitan nyamuk Aedes. Penelitian ini bertujuan untuk memperoleh model dengan metode regresi Poisson untuk jumlah kasus DBD yang meninggal di Kota Padang dan mengetahui faktor apa saja yang mempengaruhinya. Regresi Poisson ini digunakan untuk kejadian yang relatif jarang terjadi. Faktor yang diduga mempengaruhi DBD tersebut adalah faktor lingkungan, diantaranya persentase rumah sehat, persentase sarana air bersih yang memenuhi syarat, persentase rumah ber-Prilaku Hidup Bersih Sehat (ber-PHBS), persentase pengelolaan sampah yang memenuhi syarat dan persentase jamban yang memenuhi syarat. Kriteria pemilihan model terbaik yang digunakan adalah AIC dan BIC. Faktor yang berpengaruh terhadap jumlah kasus DBD yang meninggal adalah Persentase rumah sehat dan persentase rumah ber-Prilaku Hidup Bersih Sehat (ber-PHBS).Kata Kunci: Regresi Poisson, Demam Berdarah Dengue (DBD), Maximum Likelihood Estimator(MLE)
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Purqon, Khairul, Rina Widyasari i Ismail Husein. "PENERAPAN POISSON INVERSE GAUSSIAN REGRESSION UNTUK MEMODELKAN LAMA RAWAT INAP PASIEN DEMAM BERDARAH DENGUE (DBD) UPTDK. RSU. HAJI MEDAN PEMERINTAHAN PROVINSI SUMATERA UTARA". Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 5, nr 1 (30.04.2024): 207–15. http://dx.doi.org/10.46306/lb.v5i1.559.
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Dengue fever is one of the dangerous diseases that can threaten human life if not treated seriously. Dengue hemorrhagic fever is one of the health problems that exist in the community where the number of sufferers tends to increase and the spread is very wide. Dengue hemorrhagic fever (DHF) is a disease caused by infection with the DEN-1, DEN-2, DEN-3 or DEN-4 viruses transmitted to the bite of Aedes aegypti and Aedes Albopictus mosquitoes before it was infected by the dengue virus by dengue sufferers. Aedes aegypti mosquitoes become more infective for 8-12 days after sucking blood from dengue patients before. However, for now only researchers take only a few factors that make the length of stay of dengue patients in UPTDK. RSU. Hajj Medan Government of North Sumtera Province. In this study, the independent variables used were Patient Age Value (), Platelet value (), Leukocyte value (), and Hemoglobin value () in UPTDK. RSU. Haji Medan Government of North Sumtera Province using Poisson Inverse Gaussian Regression (PIGR). Poisson Inverse Gaussian Regression (PIGR) is a form of regression from mixed poisson designed on enumeration data with overdispersion cases. Therefore, research using the Poisson Inverse Gaussian Regression method can be carried out. The Poisson Inverse Gaussian Regression model formed is with a very significant influential variable is the Leukocyte value
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NEWALL, A. T., C. VIBOUD i J. G. WOOD. "Influenza-attributable mortality in Australians aged more than 50 years: a comparison of different modelling approaches". Epidemiology and Infection 138, nr 6 (27.11.2009): 836–42. http://dx.doi.org/10.1017/s095026880999118x.
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SUMMARYThis study aimed to compare systematically approaches to estimating influenza-attributable mortality in older Australians. Using monthly age-specific death data together with viral surveillance counts for influenza and respiratory syncytial virus, we explored two of the most frequently used methods of estimating excess influenza-attributable disease: Poisson and Serfling regression models. These approaches produced consistent age and temporal patterns in estimates of influenza-attributable mortality in older Australians but some variation in the magnitude of the disease burden. Of Australians aged >50 years, average annual estimated influenza-attributable deaths (all cause) ranged from 2314 to 3457 for the Serfling and Poisson regression models, respectively. The excess influenza-attributable disease burden was substantial under all approaches.
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Kondo Lembang, Ferry, Eysye Alchi Nara, Francis Yunito Rumlawang i Mozart Winston Talakua. "PEMODELAN PENGARUH IKLIM TERHADAP ANGKA KEJADIAN DEMAM BERDARAH DI KOTA AMBON MENGGUNAKAN METODE REGRESI GENERALIZED POISSON". Indonesian Journal of Statistics and Its Applications 3, nr 3 (31.10.2019): 341–51. http://dx.doi.org/10.29244/ijsa.v3i3.474.
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Dengue Hemorrhagic Fever (DHF) is one of the dreaded diseases of the transition season. DHF is a disease found in tropical and subtropical regions that caused by Dengue virus which is transmitted through Aedes mosquitoes. According to the World Health Organization (WHO) data, it is stated that Indonesia is the country with the highest dengue fever case in Southeast Asia. The incidence of dengue fever in Indonesia tends to increase in the middle of the rainy season, and one of the regions in Indonesia with the high level of rainfall intensity is Ambon City. DHF cases in Ambon city increase from year to year due to the last five years the intensity of rainfall is very high. Therefore, this study aims to identify climate factors that affect the incidence of DHF in Ambon City by using Generalized Poisson Regression method. Generalized Poisson Regression is appropriately considered to analyze the causing factors DHF incidence because the rating case of DHF is usually the count data that following the Poisson distribution. The results showed that the smallest AIC value for the Generalized Poisson Regression model was 75.842 with significant variables is DHF in the city of Ambon were one month earlier, air humidity, rainfall, and air humidity two months earlier.
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Kim, Taeho, Benjamin Lieberman, George Luta i Edsel A. Peña. "Prediction Regions for Poisson and Over-Dispersed Poisson Regression Models with Applications in Forecasting the Number of Deaths during the COVID-19 Pandemic". Open Statistics 2, nr 1 (1.01.2021): 81–112. http://dx.doi.org/10.1515/stat-2020-0106.
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Abstract Motivated by the Coronavirus Disease (COVID-19) pandemic, which is due to the SARS-CoV-2 virus, and the important problem of forecasting the number of daily deaths and the number of cumulative deaths, this paper examines the construction of prediction regions or intervals under the no-covariate or intercept-only Poisson model, the Poisson regression model, and a new over-dispersed Poisson regression model. These models are useful for settings with events of interest that are rare. For the no-covariate Poisson and the Poisson regression model, several prediction regions are developed and their performances are compared through simulation studies. The methods are applied to the problem of forecasting the number of daily deaths and the number of cumulative deaths in the United States (US) due to COVID-19. To examine their predictive accuracy in light of what actually happened, daily deaths data until May 15, 2020 were used to forecast cumulative deaths by June 1, 2020. It was observed that there is over-dispersion in the observed data relative to the Poisson regression model. A novel over-dispersed Poisson regression model is therefore proposed. This new model, which is distinct from the negative binomial regression (NBR) model, builds on frailty ideas in Survival Analysis and over-dispersion is quantified through an additional parameter. It has the flavor of a discrete measurement error model and with a viable physical interpretation in contrast to the NBR model. The Poisson regression model is a hidden model in this over-dispersed Poisson regression model, obtained as a limiting case when the over-dispersion parameter increases to infinity. A prediction region for the cumulative number of US deaths due to COVID-19 by October 1, 2020, given the data until September 1, 2020, is presented. Realized daily and cumulative deaths values from September 1st until September 25th are compared to the prediction region limits. Finally, the paper discusses limitations of the proposed procedures and mentions open research problems. It also pinpoints dangers and pitfalls when forecasting on a long horizon, especially during a pandemic where events, both foreseen and unforeseen, could impact point predictions and prediction regions.
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Audina, Yurid, Rina Filia Sari i Rina Widyasari. "RELATIVE RISK ANALYSIS OF THE SPREAD OF COVID-19 VIRUS IN MEDAN CITY BY SPATIAL AND NON-SPATIAL APPROACHES". ZERO: Jurnal Sains, Matematika dan Terapan 6, nr 2 (30.01.2023): 45. http://dx.doi.org/10.30829/zero.v6i2.14557.
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<span lang="EN">The city of Medan is the city with the highest cases of COVID-19 virus among cities in North Sumatra. This study was conducted to analyze the relative risk level for the spread of the COVID-19 virus. Estimation of relative risk is a statistic in disease mapping that is used to determine the distribution of disease. Relative risk estimation can be estimated using a direct estimator model or Standardized Morbility ratio and a small area estimation model using Bayesian Conditional Autoregressive (CAR) with the Poisson-Gamma model. The Poisson-Gamma model is one of the models in estimating small areas in the form of count data which is suitable for use in disease mapping cases. This study aims to find the relative risk value as the basis for mapping the spread of the COVID-19 virus in the city of Medan using the Standardized Morbility Ratio and Bayesian Condition Autoregressive models. And look for the value of the Central Error Squared (KTG) / Mean Squared Error (MSE) as a comparison which model is more efficient in estimating this research. Condition Autoregressive models. And look for the value of the Central Error Squared (KTG) / Mean Squared Error (MSE) as a comparison which model is more efficient in estimating this research.</span>
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Spackman, Erica, Sasidhar Malladi, Amos Ssematimba i Christopher B. Stephens. "Assessment of replicate numbers for titrating avian influenza virus using dose-response models". Journal of Veterinary Diagnostic Investigation 31, nr 4 (25.05.2019): 616–19. http://dx.doi.org/10.1177/1040638719853851.
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Embryonating chicken eggs (ECEs) are among the most sensitive laboratory host systems for avian influenza virus (AIV) titration, but ECEs are expensive and require space for storage and incubation. Therefore, reducing ECE use would conserve resources. We utilized statistical modeling to evaluate the accuracy and precision of AIV titration with 3 instead of 5 ECEs for each dilution by the Reed–Muench method for 50% endpoint calculation. Beta-Poisson and exponential dose-response models were used in a simulation study to evaluate observations from actual titration data from 18 AIV isolates. The reproducibility among replicates of a titration was evaluated with one AIV isolate titrated in 3 replicates with the beta-Poisson, exponential, and Weibull dose-response models. The standard deviation (SD) of the error between input and estimated virus titers was estimated with Monte Carlo simulations using the fitted dose-response models. Good fit was observed with all models that were utilized. Reducing the number of ECEs per dilution from 5 to 3 resulted in the width of the 95% confidence interval increasing from ±0.64 to ±0.75 log1050% ECE infectious doses (EID50) and the SD of the error increased by 0.03 log10EID50. Our study suggests that using fewer ECEs per dilution is a viable approach that will allow laboratories to reduce costs and improve efficiency.
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Sinaga, Juhenni Putri, i Ujian Sinulingga. "Poisson Regression Modeling Case Study Dengue Fever in Medan City in 2019". Journal of Mathematics Technology and Education 1, nr 1 (31.12.2021): 94–102. http://dx.doi.org/10.32734/jomte.v1i1.7500.
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Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus carried by the Aedes aegypti or Aedes albopictus mosquito which is spread in Southeast Asia. Medan City is one of the endemic areas for dengue fever in North Sumatra Province. This study aims to model the variable cases of dengue fever and determine the factors that have a significant effect on cases of dengue fever in the city of Medan. The method used in modeling the DHF case variable is the Poisson regression method with the response variable (Y) namely the number of DHF cases in Medan City, while the predictor variables are population density, number of health workers, number of health facilities, area height, and average waste production. In Poisson regression analysis, the response variable (Y) must meet the assumption of equidispersion. However, the assumption is often violated, namely overdispersion. Then Negative Binomial Regression was chosen as a non-linear model derived from the Poisson-gamma mixture distribution which is the application of the Generalized Linear Model (GLM) which describes the relationship between the response variable (Y) and the predictor variable (X).
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Chao, Lin, Camilla U. Rang i Linda E. Wong. "Distribution of Spontaneous Mutants and Inferences about the Replication Mode of the RNA Bacteriophage φ6". Journal of Virology 76, nr 7 (1.04.2002): 3276–81. http://dx.doi.org/10.1128/jvi.76.7.3276-3281.2002.
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ABSTRACT When a parent virus replicates inside its host, it must first use its own genome as the template for replication. However, once progeny genomes are produced, the progeny can in turn act as templates. Depending on whether the progeny genomes become templates, the distribution of mutants produced by an infection varies greatly. While information on the distribution is important for many population genetic models, it is also useful for inferring the replication mode of a virus. We have analyzed the distribution of mutants emerging from single bursts in the RNA bacteriophage φ6 and find that the distribution closely matches a Poisson distribution. The match suggests that replication in this bacteriophage is effectively by a stamping machine model in which the parental genome is the main template used for replication. However, because the distribution deviates slightly from a Poisson distribution, the stamping machine is not perfect and some progeny genomes must replicate. By fitting our data to a replication model in which the progeny genomes become replicative at a given rate or probability per round of replication, we estimated the rate to be very low and on the on the order of 10−4. We discuss whether different replication modes may confer an adaptive advantage to viruses.
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Owusu, Gabriel, Han Yu i Hong Huang. "Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories". AIMS Public Health 9, nr 4 (2022): 703–17. http://dx.doi.org/10.3934/publichealth.2022049.
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<abstract> <p>The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.</p> </abstract>
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Rosales, Juan Carlos, i Betina Abad. "Modelling by Generation of Poisson Distributed Numbers of First Historical Zika Outbreak in Salta, Argentina". Asian Journal of Probability and Statistics 26, nr 4 (17.04.2024): 22–34. http://dx.doi.org/10.9734/ajpas/2024/v26i4606.
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Aims/ Objectives:: In this work we describe the first historical Zika virus outbreak recorded in Salta, Argentina, in the year 2017, through Monte Carlo-type simulations using the Poisson model. Later we made comparisons with previous results.Study Design: Retrospective-descriptive studies and stochastic computational experiment analysis Place and Duration of Study: Department of Mathematic, Faculty of Exact Sciences. National University of Salta, Argentina, from March 2021 to December 2023.Methodology: Descriptive and computational experiment analysis. Parameter estimation by Maximum Likelihood and Simulation of type Monte Carlo.Results: We describe the probabilistic behavior through Monte Carlo simulations of the first historical outbreak of Zika in Salta Argentina, 2017. Based on the data of registered Zika cases, we estimate a probabilistic Poisson model with parameter\(\hat{\lambda}\) = 13:092 casesweek-1 and confidence interval 95%CI [11:889- 15:110]. Finally, by computational experiments we generate epidemic outbreaks with 20 runs. The computational experiments shows that, from a qualitative point of view, the descriptions of the outbreak are qualitatively acceptable and they were not better than the probabilistic model obtained in a previous study. However, from the statistical point of view, carrying out computational experiments of 10 comparative runs in each model, the models provide simulations of epidemic outbreaks by Zika virus, for this region of Salta, Argentina, that do not differ significantly at a confidence level of 5%.
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BOLLAERTS, K., J. ANTOINE, V. VAN CASTEREN, G. DUCOFFRE, N. HENS i S. QUOILIN. "Contribution of respiratory pathogens to influenza-like illness consultations". Epidemiology and Infection 141, nr 10 (6.12.2012): 2196–204. http://dx.doi.org/10.1017/s0950268812002506.
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SUMMARYInfluenza-like illnesses (ILIs) are caused by several respiratory pathogens. These pathogens show weak to strong seasonal activity implying seasonality in ILI consultations. In this paper, the contribution of pathogens to seasonality of ILI consultations was statistically modelled. Virological count data were first smoothed using modulation models for seasonal time series. Second, Poisson regression was used regressing ILI consultation counts on the smoothed time series. Using ratios of the estimated regression parameters, relative measures of the underreporting of pathogens were obtained. Influenza viruses A and B, parainfluenza virus and respiratory syncytial virus (RSV) significantly contributed to explain the seasonal variation in ILI consultations. We also found that RSV was the least and influenza virus A is the most underreported pathogen in Belgian laboratory surveillance. The proposed methods and results are helpful in interpreting the data of clinical and laboratory surveillance, which are the essential parts of influenza surveillance.
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Martinez, Matias, Horacio Vargas-Guzman i Christopher D. Cooper. "Implicit Solvent Calculations at Large-Scale Virus-Level Poisson-Boltzmann and Multiscale Simulations for Electrostatics". Biophysical Journal 116, nr 3 (luty 2019): 291a. http://dx.doi.org/10.1016/j.bpj.2018.11.1574.
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RAJATONIRINA, S., B. RAKOTOSOLOFO, F. RAKOTOMANANA, L. RANDRIANASOLO, M. RATSITOHARINA, H. RAHARINANDRASANA, J. M. HERAUD i V. RICHARD. "Excess mortality associated with the 2009 A(H1N1)v influenza pandemic in Antananarivo, Madagascar". Epidemiology and Infection 141, nr 4 (20.07.2012): 745–50. http://dx.doi.org/10.1017/s0950268812001215.
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SUMMARYIt is difficult to assess the mortality burden of influenza epidemics in tropical countries. Until recently, the burden of influenza was believed to be negligible in Africa. We assessed the impact of the 2009 influenza epidemic on mortality in Madagascar by conducting Poisson regression analysis on mortality data from the deaths registry, after the first wave of the 2009 A(H1N1) virus pandemic. There were 20% more human deaths than expected in Antananarivo, Madagascar in November 2009, with excess mortality in the ⩾50 years age group (relative risk 1·41). Furthermore, the number of deaths from pulmonary disease was significantly higher than the number of deaths from other causes during this pandemic period. These results suggest that the A(H1N1) 2009 virus pandemic may have been accompanied by an increase in mortality.
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ADEGBOYE, O. A., i D. KOTZE. "Epidemiological analysis of spatially misaligned data: a case of highly pathogenic avian influenza virus outbreak in Nigeria". Epidemiology and Infection 142, nr 5 (4.09.2013): 940–49. http://dx.doi.org/10.1017/s0950268813002136.
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SUMMARYThis research is focused on the epidemiological analysis of the transmission of the highly pathogenic avian influenza (HPAI) H5N1 virus outbreak in Nigeria. The data included 145 outbreaks together with the locations of the infected farms and the date of confirmation of infection. In order to investigate the environmental conditions that favoured the transmission and spread of the virus, weather stations were realigned with the locations of the infected farms. The spatial Kolmogorov–Smirnov test for complete spatial randomness rejects the null hypothesis of constant intensity (P < 0·0001). Preliminary exploratory analysis showed an increase in the incidence of H5N1 virus at farms located at high altitude. Results from the Poisson log-linear conditional intensity function identified temperature (−0·9601) and wind speed (0·6239) as the ecological factors that influence the intensity of transmission of the H5N1 virus. The model also includes distance from the first outbreak (−0·9175) with an Akaike's Information Criterion of −103·87. Our analysis using a point process model showed that geographical heterogeneity, seasonal effects, temperature, wind as well as proximity to the first outbreak are very important components of spread and transmission of HPAI H5N1.
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Jiménez, Martha, Humberto Ríos, Pilar Gómez, María Elena Tavera, Raúl Junior Sandoval, Francisco Pérez, Ma de los Ángeles Martínez i in. "Analysis of application of covid-19 vaccine in Mexico city by age and gender groups in the second wave of the pandemic". International Journal of Vaccines & Vaccination 7, nr 1 (2022): 3–7. http://dx.doi.org/10.15406/ijvv.2022.07.00112.
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Considering the importance of giving continuity to economic activities that have been partially suspended by the global SARS-CoV-2 virus pandemic, the impact of virus contagions with the application of the vaccine was analyzed in Mexico City, in men and women by age groups in the second wave of the virus, from 28-June-2021 to 01-September-2021. Two Poisson regression panel models were performed by random effects by gender and age groups and the variables: infections, dose, applied vaccine, and diseases. A decrease in contagions was found with the AstraZeneca, CoronaVac, Pfizer, and Sputnik vaccines for men aged 18 to 29 years, AstraZeneca and CoronaVac for women aged 18 to 29; and AstraZeneca and Pfizer for men and women ages 50 to 59, as well as Sputnik for men and women over 60. It is concluded that COVID-19 vaccines act differently according to gender and age group. Furthermore, the vaccine that helped reduce contagions with the greatest impact was AstraZeneca for the group of 50 to 59 years old.
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Fedotov, Sergei, Dmitri Alexandrov, Ilya Starodumov i Nickolay Korabel. "Stochastic Model of Virus–Endosome Fusion and Endosomal Escape of pH-Responsive Nanoparticles". Mathematics 10, nr 3 (26.01.2022): 375. http://dx.doi.org/10.3390/math10030375.
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In this paper, we set up a stochastic model for the dynamics of active Rab5 and Rab7 proteins on the surface of endosomes and the acidification process that govern the virus–endosome fusion and endosomal escape of pH-responsive nanoparticles. We employ a well-known cut-off switch model for Rab5 to Rab7 conversion dynamics and consider two random terms: white Gaussian and Poisson noises with zero mean. We derive the governing equations for the joint probability density function for the endosomal pH, Rab5 and Rab7 proteins. We obtain numerically the marginal density describing random fluctuations of endosomal pH. We calculate the probability of having a pH level inside the endosome below a critical threshold and therefore the percentage of viruses and pH-responsive nanoparticles escaping endosomes. Our results are in good qualitative agreement with experimental data on viral escape.
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Garcia, Danielle R., Felipe R. Souza, Ana P. Guimarães, Martin Valis, Zbyšek Pavelek, Kamil Kuca, Teodorico C. Ramalho i Tanos C. C. França. "In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus". Pharmaceuticals 14, nr 10 (9.10.2021): 1027. http://dx.doi.org/10.3390/ph14101027.
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Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The proposed structures were subjected to docking calculations, molecular dynamics simulations, and free energy calculations, using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method, inside the active sites of VarTMPK and human TMPK (HssTMPK). The docking and molecular dynamic studies pointed to structures 2, 3, 4, 6, and 9 as more selective towards VarTMPK. In addition, the free energy data calculated through the MM-PBSA method, corroborated these results. This suggests that these compounds are potential selective inhibitors of VarTMPK and, thus, can be considered as template molecules to be synthesized and experimentally evaluated against smallpox.
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TÜZÜN, Burak, Koray SAYİN i Hilmi ATASEVEN. "Could Momordica Charantia Be Effective In The Treatment of COVID19?" Cumhuriyet Science Journal 43, nr 2 (29.06.2022): 211–20. http://dx.doi.org/10.17776/csj.1009906.
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One of the deadliest diseases is the SARS-CoV-2 virus, today. The rate of spread of this virus is very high. Momordica Charantia extracts studied for this virus. The inhibitory activities of 96 components in the extract of Momordica Charantia were compared against the SARS-CoV-2 virus. Molecular docking method was initially used for this comparison. ADME/T analysis of the inhibitors with the highest inhibitory activity was performed using the results obtained from these calculations. The molecular docking calculations of the molecule with the highest inhibitory activity were tried to be supported by MM-PBSA calculations. The molecular mechanics Poisson-Boltzmann surface binding free energy values of area (MM-PBSA) calculations study interactions between inhibitor molecules and SARS-CoV-2 virus proteins at 100 ps. Finally, the molecules with the highest inhibitory activity were compared with FDA approved drugs. As a result of the made molecular docking calculations, the docking score parameter is Karaviloside III with -9.36, among the extracts of momordica charantia, which has the most negative value. The Gibbs free energy value of the Karaviloside III against 6X6P protein with the best docking score value was calculated. This value is -477143.61±476.53. As a result of the comparison of inhibitory activities of extracts of Momordica charantia against SARS-CoV-2 virus, it has been observed that the Karaviloside III molecule has higher inhibitory activity than other melodies and FDA drugs.
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Olarte Garciá, Julián Alejandro, i Anibal Muñoz Loaiza. "Analysis of Strategies for Preventing and Controlling the Chikungunya Virus". Revista Facultad de Ciencias Básicas 16, nr 1 (19.03.2021): 57–68. http://dx.doi.org/10.18359/rfcb.4341.
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Alternatives to stop chikungunya outbreaks are oriented to vector control and developing a specific treatment and a preventive vaccine. Environmental control and mosquito bite prevention are undoubtedly essential to decrease the disease burden, but Aedes vectors continue to expand geographically and re-emerge. So, vaccination is proposed to respond to this etiology and recognized as a pressing need for affected countries. A mathematical host-vector model, including asymptomatic population, vector control, and vaccination (assuming the existence of a safe protective vaccine against the chikungunya virus), is suggested to analyze the effects of these efforts. Poisson distribution is applied to interpret the basic reproduction number. Then vaccination and vector control thresholds are established to prescribe the most effective protection measures against exposure to the chikungunya virus. In conclusion, it is advisable to continue with integrated control to reduce the economic impact of relevant public health responses and mitigate other infections since Aedes is a transmitter of other arboviruses such as dengue, Zika, and Mayaro. Furthermore, vaccinating all individuals in a community could be a costly and gradual process.
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Zhang, Qiuxian, i Hecheng Wang. "Scrutiny of the mechanism of β-amyloid protein captures HSV-2 to protect the brain infection". E3S Web of Conferences 261 (2021): 02069. http://dx.doi.org/10.1051/e3sconf/202126102069.
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Alzheimer’s disease (AD) is an age-related neurodegenerative disorder. β-amyloid protein (Aβ) is the key protein which involved in AD. But the physiological function of Aβ is needed to be investigated. Many experimental studies have shown that Aβ could bind to glycoproteins D (gD) on the surface of the herpes virus. However the mechanism is still unclear. In the present study, we elucidate the molecular mechanism of the interaction between Aβ and gD of herpes simplex virus type 2 (HSV-2) by molecular docking and molecular dynamics simulation. Molecular dynamics simulations displayed that Aβ could stably bind to the HSV-2 gD owing to the presence of several interactions. Analysis binding free energy by molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) method revealed that hot residues including Glu3, Glu11, Glu22 and Ala42 of Aβ1-42 were involved in binding with HSV-2 gD. Thus, the HSV-2 gD can be entrapped by Aβ which will be utilized for prevent and therapy of AD in future.
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Jung, Sun Jae, Sung-Shil Lim i Jin-Ha Yoon. "Fluctuations in influenza-like illness epidemics and suicide mortality: A time-series regression of 13-year mortality data in South Korea". PLOS ONE 16, nr 2 (12.02.2021): e0244596. http://dx.doi.org/10.1371/journal.pone.0244596.
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Aims We explored the association between influenza epidemic and suicide mortality rates in a large population using a time-series regression of 13-year mortality data in South Korea. Methods Weekly suicide mortalities and influenza-like illness (ILI) were analyzed using time series regression. Regression coefficient for suicide mortality based on percentage change of ILI was calculated using a quasi-Poisson regression. Non-linear distributed lag models with quadratic function up to 24 weeks were constructed. Results The association between ILI and suicide mortality increased significantly up to 8 weeks post-influenza diagnosis. A significant positive association between ILI and suicide mortality was observed from 2009, when a novel influenza A(H1N1)pdm09 virus provoked a worldwide pandemic. No meaningful association between these factors was observed before 2009. Conclusion There was a significant positive relationship between ILI and suicide mortality after 2009, when a novel influenza A(H1N1)pdm09 virus provoked a worldwide pandemic.
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Aida, Liza Nur, i Ria Dhea Layla Nur Karisma. "Measles Disease Model using Censored Hurdle Negative Binomial Regression in East Java". Proceedings of the International Conference on Green Technology 11, nr 1 (3.11.2021): 20. http://dx.doi.org/10.18860/icgt.v11i1.1396.
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Abstract- Measles is an infectious disease caused by measles virus and contagious. In recent years, especially in Indonesia, the number of measles rates have decreased at 2021 then some observations were worth zero. Hurdle Negative Binomial Regression is a method that used to overcome excess zero and over dispersion. Furthermore, count data is a data with non-negative integers that showed the number of event then it unable to use Poisson Regression. The aim of the study is to obtain measles model using HNBR in Eat Java. Based on the result of study, the factors that influence are vitamin A distribution, malnutrition in toddlers, and population density in East Java.
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FRENCH, N. P., L. KELLY, R. JONES i D. CLANCY. "Dose-response relationships for foot and mouth disease in cattle and sheep". Epidemiology and Infection 128, nr 2 (kwiecień 2002): 325–32. http://dx.doi.org/10.1017/s0950268801006446.
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The relationships between the inhaled dose of foot and mouth disease virus and the outcomes of infection and disease were examined by fitting dose-response models to experimental data. The parameters for both the exponential and beta-poisson models were estimated using maximum likelihood and Bayesian methods. The median probability of infection given a single inhaled TCID50 was estimated to be 0·031 with 95% Bayesian credibility intervals (CI) of 0·018–0·052 for cattle, and 0·045 (CI = 0·024–0·080) for sheep. These estimates were used to construct dose-response curves and uncertainty distributions for use in quantitative risk assessments.
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Ikeno, Ryo, Eiko Yamada, Sayaka Yamazaki, Tomoyuki Ueda, Masaki Nagata, Ritsuo Takagi i Shingo Kato. "Factors contributing to salivary human immunodeficiency virus type-1 levels measured by a Poisson distribution-based PCR method". Journal of International Medical Research 46, nr 3 (9.11.2017): 996–1007. http://dx.doi.org/10.1177/0300060517728652.
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Objective To elucidate the mechanism underlying secretion of human immunodeficiency virus type 1 (HIV-1) into the oral cavity, by examining the relationships between various oral and systemic factors and the viral load in saliva. Methods Plasma and saliva samples from HIV-1 infected patients were assayed using the COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 Test, version 1.0 and a Poisson distribution-based polymerase chain reaction (PCR) method for quantifying HIV-1 RNA and DNA. Results Forty-four pairs of samples were obtained from 18 patients. Salivary viral load was approximately 10% of the plasma viral load, but higher than the plasma load in two patients. The salivary viral DNA load was < 1% of the total HIV-1 nucleic acid load except in one patient who had more viral DNA than RNA. Multiple regression analysis showed that salivary viral load was significantly correlated with plasma viral load (partial correlation coefficient, 0.90) and the community periodontal index (–0.63). Conclusions The present results suggest that excretion through salivary glands, but not occult bleeding, may be a major pathway of HIV-1 into the oral cavity.
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Bald, J. G., R. Iltis, S. M. Schneider, D. V. Gokhale i P. R. Desjardins. "Association of logistic and Poisson models of infection with some physical characteristics of a single component plant virus". Journal of Virological Methods 27, nr 1 (styczeń 1990): 11–28. http://dx.doi.org/10.1016/0166-0934(90)90142-3.
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Votapka, Lane W., Luke Czapla, Maxim Zhenirovskyy i Rommie E. Amaro. "DelEnsembleElec: Computing Ensemble-Averaged Electrostatics Using DelPhi". Communications in Computational Physics 13, nr 1 (styczeń 2013): 256–68. http://dx.doi.org/10.4208/cicp.170711.111111s.
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AbstractA new VMD plugin that interfaces with DelPhi to provide ensemble-averaged electrostatic calculations using the Poisson-Boltzmann equation is presented. The general theory and context of this approach are discussed, and examples of the plugin interface and calculations are presented. This new tool is applied to systems of current biological interest, obtaining the ensemble-averaged electrostatic properties of the two major influenza virus glycoproteins, hemagglutinin and neuraminidase, from explicitly solvated all-atom molecular dynamics trajectories. The differences between the ensemble-averaged electrostatics and those obtained from a single structure are examined in detail for these examples, revealing how the plugin can be a powerful tool in facilitating the modeling of electrostatic interactions in biological systems.
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Tereshko, Lauren, Xiaohui Zhao, Jake Gagnon, Tinchi Lin, Trevor Ewald, Yu Wang, Marina Feschenko i Cullen Mason. "A novel method for quantitation of AAV genome integrity using duplex digital PCR". PLOS ONE 18, nr 12 (14.12.2023): e0293277. http://dx.doi.org/10.1371/journal.pone.0293277.
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Recombinant adeno-associated virus (rAAV) vectors have become a reliable strategy for delivering gene therapies. As rAAV capsid content is known to be heterogeneous, methods for rAAV characterization are critical for assessing the efficacy and safety of drug products. Multiplex digital PCR (dPCR) has emerged as a popular molecular approach for characterizing capsid content due to its high level of throughput, accuracy, and replicability. Despite growing popularity, tools to accurately analyze multiplexed data are scarce. Here, we introduce a novel statistical model to estimate genome integrity from duplex dPCR assays. This work demonstrates that use of a Poisson-multinomial mixture distribution significantly improves the accuracy and quantifiable range of duplex dPCR assays over currently available models.
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Menezes, Gabriela de Lima, Marielena Vogel Saivish, Lívia Sacchetto, Gislaine Celestino Dutra da Silva, Igor da Silva Teixeira, Natalia Franco Bueno Mistrão, Maurício Lacerda Nogueira i in. "Exploring Quercetin Hydrate’s Potential as an Antiviral Treatment for Oropouche Virus". Biophysica 3, nr 3 (12.08.2023): 485–500. http://dx.doi.org/10.3390/biophysica3030032.
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The Oropouche virus is an orthobunyavirus responsible for causing Oropouche fever, a disease that primarily affects thousands of people in South and Central America. Currently, no specific antiviral treatments or vaccines are available against this virus, highlighting the urgent need for safe, affordable, and effective therapies. Natural products serve as an important source of bioactive compounds, and there is growing interest in identifying natural bioactive molecules that could be used for treating viral diseases. Quercetin hydrate is a compound classified as a flavonoid, which has garnered scientific attention due to its potential health benefits and its presence in various plant-based foods. In this study, we aim to evaluate the in vitro antiviral activity of quercetin hydrate against the Oropouche virus (OROV). Furthermore, we intend to explore its mode of action through in silico approaches. The cytotoxicity and antiviral activity of the compound were assessed using Vero cells. In addition, in silico studies were also performed through molecular docking, molecular dynamics simulations, Molecular Mechanics Poisson–Boltzmann surface area (MM/PBSA), and quantum-mechanical analysis in order to evaluate the interaction with the Gc protein of OROV. The assay revealed that the compound was highly active against the virus, inhibiting OROV with an EC50 value of 53.5 ± 26.5 µM under post-infection treatment conditions. The present study demonstrates that the compound is a promising antiviral agent; however, the mechanisms of action proposed in this study need to be experimentally verified by future assays.
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Sither, Charles B., John M. Sither i Brian D. Byrd. "A Comparison of Oak Leaf and Fescue Hay Infusion-Baited Gravid Trap Collections—An Analysis Steeped in the Context of La Crosse Virus Vector Surveillance Effectiveness". Journal of the American Mosquito Control Association 39, nr 2 (1.06.2023): 138–41. http://dx.doi.org/10.2987/23-2116.
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ABSTRACT Neuroinvasive La Crosse virus disease remains the primary cause of pediatric arboviral encephalitis in the USA. In spite of the persistent public health burden, there are limited entomologic surveillance options that target both native and invasive La Crosse virus (LACV) vectors. In this study we used Reiter/Cummings tacklebox gravid traps to compare white oak (Quercus alba) and hay (predominately Festuca arundinacea) infusions within a LACV-endemic area of western North Carolina. Paired gravid traps (approximately 1,728 total trap-hours for each infusion) yielded 485 mosquitoes, with 3 species (Aedes japonicus [n = 265], Ae. triseriatus [n = 156], and Culex restuans [n = 45]) accounting for 96.1% of the total collection. The hay-infusion traps collected 2.5 times more Ae. triseriatus and 1.3 times more Ae. japonicus than the oak-infusion traps. The sum differences in overall collections for these 2 species by infusion type were statistically significant (χ2 = 9.61, df = 1, P = 0.0019). Poisson ratio tests to compare capture rates suggest that hay infusions were more effective for capturing Ae. triseriatus, but that hay and white oak leaf infusions had equivocal capture rates for Ae. japonicus (an invasive LACV accessory vector) and Cx. restuans (an enzootic West Nile virus vector). These results are discussed in the context of operational considerations for LACV vector surveillance.
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Wendelboe, Aaron M., Ozair H. Naqvi, Mary Williams, Heather Hollen, Kaitlin McGrew, Peng Li, Terrainia Harris i Ann F. Chou. "Opioid and other drug use and drug-related mortality as indicators of Hepatitis C and Human Immunodeficiency Virus in Oklahoma". PLOS ONE 19, nr 5 (9.05.2024): e0301442. http://dx.doi.org/10.1371/journal.pone.0301442.
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Objectives Outbreaks of injection drug use (IDU)-associated infections have become major public health concerns in the era of the opioid epidemic. This study aimed to (1) identify county-level characteristics associated with acute HCV infection and newly diagnosed IDU-associated HIV in Oklahoma and (2) develop a vulnerability index using these metrics. Methods This study employs a county-level ecological design to examine those diagnosed with acute or chronic HCV or newly diagnosed IDU-associated HIV. Poisson regression was used to estimate the association between indicators and the number of new infections in each county. Primary outcomes were acute HCV and newly diagnosed IDU-associated HIV. A sensitivity analysis included all HCV (acute and chronic) cases. Three models were run using variations of these outcomes. Stepwise backward Poisson regression predicted new infection rates and 95% confidence intervals for each county from the final multivariable model, which served as the metric for vulnerability scores. Results Predictors for HIV-IDU cases and acute HCV cases differed. The percentage of the county population aged 18–24 years with less than a high school education and population density were predictive of new HIV-IDU cases, whereas the percentage of the population that was male, white, Pacific Islander, two or more races, and people aged 18–24 years with less than a high school education were predictors of acute HCV infection. Counties with the highest predicted rates of HIV-IDU tended to be located in central Oklahoma and have higher population density than the counties with the highest predicted rates of acute HCV infection. Conclusions There is high variability in county-level factors predictive of new IDU-associated HIV infection and acute HCV infection, suggesting that different public health interventions need to be tailored to these two case populations.
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Paraboni, Marisa Lúcia Romani, Marina Dallagasperina Sbeghen, Fernando Herz Wolff i Leila Beltrami Moreira. "Risk Factors for Infection with Different Hepatitis C Virus Genotypes in Southern Brazil". Scientific World Journal 2012 (2012): 1–6. http://dx.doi.org/10.1100/2012/946954.
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Objectives. To investigate the proportion of different genotypes in countryside microregions in southern Brazil, and their association with risk factors.Methods. Cross-sectional study including a convenience sample of patients who tested positive for HCV-RNA and were referred to a regional health center for genotyping, from December 2003 to January 2008. Data were obtained through the National Disease Surveillance Data System, from laboratory registers and from patient charts. Identification of genotypes was carried out using the Restriction Fragment Length Polymorphism “in house” technique. Independent associations with genotypes were evaluated in multinomial logistic regression and prevalence rates of genotypes were estimated with modified Poisson regression.Results. The sample consisted of 441 individuals, years old, 56.5% men. Genotype 1 was observed in 41.5% (95% CI 37.9–48.1) of patients, genotype 2 in 19.3% (95% CI 15.0–23.6), and genotype 3 in 39.2% (95% CI 35.6–43.0). HCV genotype was significantly associated with gender and age. Dental procedures were associated with higher proportion of genotype 2 independently of age, education, and patient treatment center.Conclusions. The hepatitis C virus genotype 1 was the most frequent. Genotype 2 was associated with female gender, age, and dental procedure exposition.
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Satpathy, R., i S. Acharya. "Exploring the Mangrove Based Phytochemicals as Potential Viral RNA Helicase Inhibitors by in silico Docking and Molecular Dynamics Simulation Methods". Mathematical Biology and Bioinformatics 18, nr 2 (19.11.2023): 405–17. http://dx.doi.org/10.17537/2023.18.405.
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A variety of plant-derived molecular compounds from mangrove plants have attracted attention due to the discovery of their antiviral activity. It has been proven that herbal medicines based on them provide good protection against a number of pathogenic viruses. However, it is necessary to screen these effective antiviral compounds to select those that have fewer harmful side effects. This study aimed to screen several bioactive compounds from mangrove plants that could be used as a viral RNA helicase inhibitor. Fifty-nine compounds were selected from the literature and databases for initial study and screening according to Lipinski's rule of five. The resulting selected compounds were subjected to another round of screening through molecular docking studies with five different pathogenic virus RNA helicase enzymes using the Autodock Vina tool followed by ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis. In addition, the best compound-bound helicase-RNA complexes were included in 50 ns molecular dynamics simulations using Gromacs 5.1.1 software followed by molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis. This comparative study predicts that the phytochemical gedunin is an excellent inhibitor of the RNA helicase enzyme of SARS-CoV-2, followed by Japanese encephalitis virus and hepatitis C virus. The results of the study may lead to the development of antiviral compounds against the RNA helicase enzymes of pathogenic viruses.
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Hayat, Muhammad, Tian Gao, Ying Cao, Muhammad Rafiq, Li Zhuo i Yue-Zhong Li. "Identification of Prospective Ebola Virus VP35 and VP40 Protein Inhibitors from Myxobacterial Natural Products". Biomolecules 14, nr 6 (5.06.2024): 660. http://dx.doi.org/10.3390/biom14060660.
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The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified as druggable targets. In this study, we employed in silico methods comprising molecular docking, molecular dynamic simulations, and pharmacological properties to identify prospective drugs for inhibiting VP35 and VP40 proteins from the myxobacterial bioactive natural product repertoire. Cystobactamid 934-2, Cystobactamid 919-1, and Cittilin A bound firmly to VP35. Meanwhile, 2-Hydroxysorangiadenosine, Enhypyrazinone B, and Sorangiadenosine showed strong binding to the matrix protein VP40. Molecular dynamic simulations revealed that, among these compounds, Cystobactamid 919-1 and 2-Hydroxysorangiadenosine had stable interactions with their respective targets. Similarly, molecular mechanics Poisson–Boltzmann surface area (MMPBSA) calculations indicated close-fitting receptor binding with VP35 or VP40. These two compounds also exhibited good pharmacological properties. In conclusion, we identified Cystobactamid 919-1 and 2-Hydroxysorangiadenosine as potential ligands for EBOV that target VP35 and VP40 proteins. These findings signify an essential step in vitro and in vivo to validate their potential for EBOV inhibition.
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BI, P., J. E. HILLER, A. S. CAMERON, Y. ZHANG i R. GIVNEY. "Climate variability and Ross River virus infections in Riverland, South Australia, 1992–2004". Epidemiology and Infection 137, nr 10 (19.03.2009): 1486–93. http://dx.doi.org/10.1017/s0950268809002441.
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SUMMARYRoss River virus (RRV) infection is the most common notifiable vector-borne disease in Australia, with around 6000 cases annually. This study aimed to examine the relationship between climate variability and notified RRV infections in the Riverland region of South Australia in order to set up an early warning system for the disease in temperate-climate regions. Notified data of RRV infections were collected by the South Australian Department of Health. Climatic variables and monthly river flow were provided by the Australian Bureau of Meteorology and South Australian Department of Water, Land and Biodiversity Conservation over the period 1992–2004. Spearman correlation and time-series-adjusted Poisson regression analysis were performed. The results indicate that increases in monthly mean minimum and maximum temperatures, monthly total rainfall, monthly mean Southern Oscillation Index and monthly flow in the Murray River increase the likelihood, but an increase in monthly mean relative humidity decreases the likelihood, of disease transmission in the region, with different time-lag effects. This study demonstrates that a useful early warning system can be developed for local regions based on the statistical analysis of readily available climate data. These early warning systems can be utilized by local public health authorities to develop disease prevention and control activities.
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Fitzpatrick, Tiffany, Sarah A. Buchan, Sanjay Mahant, Longdi Fu, Jeffrey C. Kwong, Therese A. Stukel i Astrid Guttmann. "Pediatric Respiratory Syncytial Virus Hospitalizations, 2017-2023". JAMA Network Open 7, nr 6 (11.06.2024): e2416077. http://dx.doi.org/10.1001/jamanetworkopen.2024.16077.
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ImportanceRespiratory syncytial virus (RSV) transmission was disrupted worldwide following the COVID-19 pandemic, and further study is required to better understand these changes.ObjectiveTo compare observed and expected RSV hospital and intensive care unit (ICU) admission rates and characteristics of admitted children during the 2021-2022 and 2022-2023 seasons.Design, Setting, and ParticipantsA population-based cohort study of all children aged younger than 5 years in Ontario, Canada, July 1, 2017, through March 31, 2023, was conducted.ExposuresIndividual and neighborhood-level sociodemographic and clinical characteristics were identified from administrative data, including age, palivizumab eligibility, complex medical conditions, rurality, and living in a marginalized neighborhood.Main Outcomes and MeasuresThe main outcome was RSV-associated hospitalization. Secondary outcomes included ICU admissions, mechanical ventilation, extracorporeal membrane oxygenation, and in-hospital death. Poisson generalized estimating equations were used to model weekly age- and sex-specific hospitalization rates and estimate expected rates in the postpandemic era; adjusted rate ratios (RRs) and 95% CIs are reported.ResultsThis cohort study included approximately 700 000 children per study year. Compared with prepandemic years (2017-2018, 2018-2019, and 2019-2020), the 2021-2022 RSV season peaked slightly earlier, but overall admission rates were comparable (289.1 vs 281.4-334.6 per 100 000, or approximately 2000 admissions). The 2022-2023 season peaked a month earlier and resulted in more than twice as many hospitalizations (770.0 per 100 000; n = 4977 admissions). The proportion of children admitted to an ICU in 2022-2023 (13.9%) was slightly higher than prepandemic (9.6%-11.4%); however, the population-based rate was triple the prepandemic levels (106.9 vs 27.6-36.6 per 100 000 children in Ontario). With the exception of palivizumab-eligible children, all sociodemographic and health status characteristics were associated with lower-than-expected RSV hospitalization rates in 2021-2022. In contrast, older age of patients was associated with higher-than-expected rates in 2022-2023 (ie, 24-59 months: RR, 1.90; 95% CI, 1.35-2.66).Conclusions and RelevanceThere were notable differences in RSV epidemiologic characteristics in Ontario following the COVID-19 pandemic. It is not yet clear whether and how long atypical RSV epidemics may persist. Clinicians and program planners should consider the potential for ongoing impacts to health care capacity and RSV immunization programs.
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Chhetri, Bimal K., Olaf Berke, David L. Pearl i Dorothee Bienzle. "Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem". Journal of Veterinary Medicine 2014 (20.02.2014): 1–11. http://dx.doi.org/10.1155/2014/424138.
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The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP). In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states) by Moran’s I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran’s I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.
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Ongruk, Phatsavee, Padet Siriyasatien i Kraisak Kesorn. "New Key Factors Discovery to Enhance Dengue Fever Forecasting Model". Advanced Materials Research 931-932 (maj 2014): 1457–61. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1457.
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There are several factors that can be used to predict a dengue fever outbreak. Almost all existing research approaches, however, usually exploit the use of a basic set of core attributes to forecast an outbreak, e.g. temperature, humidity, wind speed, and rainfall. In contrast, this research identifies new attributes to improve the prediction accuracy of the outbreak. The experimental results are analyzed using a correlation analysis and demonstrate that the density of dengue virus infection rate in female mosquitoes and seasons have strong correlation with a dengue fever outbreak. In addition, the research constructs a forecast model using Poisson regression analysis. The result shows the proposed model obtains significantly low forecasting error rate when compared it against the conventional model using only temperature, humidity, wind speed, and rainfall parameters.
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Peterson, S. R., i N. J. Ashbolt. "Viral risks associated with wastewater reuse: modeling virus persistence on wastewater irrigated salad crops". Water Science and Technology 43, nr 12 (1.06.2001): 23–26. http://dx.doi.org/10.2166/wst.2001.0706.
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A model for virus decay on lettuce and carrot crops has been derived as part of a comprehensive wastewater irrigation microbial risk assessment model under development. Results from the decay modeling indicated the presence of a very persistent sub-population of viruses evidenced by an initial rapid phase of decay followed by a very slow phase. In addition, virus counts fitted a negative binomial rather than Poisson distribution indicating over-dispersion. Hence the data indicated that viruses were not uniformly distributed over the surfaces of both crops. The aim of this paper was to investigate the implications of over-dispersion and the presence of a very persistent sub-population of viruses for assessing viral illness from the consumption of lettuces and carrots irrigated with secondary treated effluent. When over-dispersion or clumping of viruses was accounted for, a significant increase in the heterogeneity in the risk estimates arose. In addition, predicted infection rates were significantly underestimated if the presence of a persistent sub-population of viruses was not considered in the decay kinetics of the risk model. Hence, both viral clumping and persistence sub-populations should be accounted for in future risk assessments of enteric viruses associated with wastewater reuse.
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Umuhoza, Therese, Julius Oyugi, James D. Mancuso i Wallace D. Bulimo. "Spatial and Spatio-Temporal Distribution of Human Respiratory Syncytial Virus, Human Parainfluenza Virus, and Human Adenoviruses Cases in Kenya 2007-2013". East African Health Research Journal 6, nr 1 (22.07.2022): 52–63. http://dx.doi.org/10.24248/eahrj.v6i1.679.
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Background: Human Respiratory Syncytial Virus (HRSV), Human Parainfluenza Virus (HPIV), and Human Adenovirus (HAdV) epidemics differ in geographical location, time, and virus type. Regions prone to infections can be identified using geographic information systems (GIS) and available methods for detecting spatial and time clusters. We sought to find statistically significant spatial and time clusters of HRSV, HPIV, and HAdV cases in different parts of Kenya. Methods: To analyse retrospective data, we used a geographical information system (GIS) and the spatial scan statistic. The information was gathered from surveillance sites and aggregated at the county level in order to identify purely spatial and Spatio-temporal clusters. To detect the presence of spatial autocorrelation, the local Moran’s I test was used. To detect the spatial clusters of HRSV, HPIV, and HAdV cases, we performed the purely spatial scan statistic. Furthermore, space-time clusters were identified using space-time scan statistics. Both spatial and space-time analyses were based on the discrete Poisson model with a pre-specified statistical significance levelof p<0.05 Results: The findings showed that HRSV, HPIV, and HAdV cases had significant autocorrelation within the study areas. Furthermore, in the Western region of the country, the three respiratory viruses had local clusters with significant positive autocorrelation (p<0.05). Statistically, the Western region had significant spatial clusters of HRSV, HPIV, and HAdV occurrence. Furthermore, the space-time analysis revealed that the HPIV primary cluster persisted in the Western region from 2007 to 2013. However, primary clusters of HRSV and HAdV were observed in the Coastal region in 2009-11 and 2008-09, respectively. Conclusion: Human respiratory syncytial virus (HRSV), human parainfluenza virus (HPIV), and human adenovirus (HAdV) hotspots (clusters) occurred in Kenya’s Western and Coastal regions from 2007 to 2013. The Western region appeared to be more prone to the occurrence of allthree respiratory viruses throughout the study period.Strategic mitigation should focus on these locations to prevent future clusters of HRSV, HPIV, and HAdV infections that could lead to epidemics.
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Labuda, Sarah M., Yanling Huo, Deborah Kacanek, Kunjal Patel, Krista Huybrechts, Jennifer Jao, Christiana Smith i in. "Rates of Hospitalization and Infection-Related Hospitalization Among Human Immunodeficiency Virus (HIV)–Exposed Uninfected Children Compared to HIV-Unexposed Uninfected Children in the United States, 2007–2016". Clinical Infectious Diseases 71, nr 2 (22.08.2019): 332–39. http://dx.doi.org/10.1093/cid/ciz820.
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Abstract Background Studies from multiple countries have suggested impaired immunity in perinatally human immunodeficiency virus (HIV)–exposed uninfected children (HEU), with elevated rates of all-cause hospitalization and infections. We estimated and compared the incidence of all-cause hospitalization and infection-related hospitalization in the first 2 years of life among HEU and HIV-unexposed uninfected children (HUU) in the United States. Among HEU, we evaluated associations of maternal HIV disease–related factors during pregnancy with risk of child hospitalization. Methods HEU data from subjects enrolled in the Surveillance Monitoring for Antiretroviral Therapy Toxicities Study (SMARTT) cohort who were born during 2006–2017 were analyzed. HUU comparison data were obtained from the Medicaid Analytic Extract database, restricted to states participating in SMARTT. We compared rates of first hospitalization, total hospitalizations, first infection-related hospitalization, total infection-related hospitalizations, and mortality between HEU and HUU using Poisson regression. Among HEU, multivariable Poisson regression models were fitted to evaluate associations of maternal HIV factors with risk of hospitalization. Results A total of 2404 HEU and 3 605 864 HUU were included in the analysis. HEU children had approximately 2 times greater rates of first hospitalization, total hospitalizations, first infection-related hospitalization, and total infection-related hospitalizations compared with HUUs. There was no significant difference in mortality. Maternal HIV disease factors were not associated with the risk of child infection or hospitalization. Conclusions Compared with HUU, HEU children in the United States have higher rates of hospitalization and infection-related hospitalization in the first 2 years of life, consistent with studies in other countries. Closer monitoring of HEU infants for infection and further elucidation of immune mechanisms is needed.