m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

Nasopharyngeal angiotensin converting enzyme 2 (ACE2) expression as a risk-factor for SARS-CoV-2 transmission in concurrent hospital associated outbreaks.

BACKGROUND: Widespread human-to-human transmission of the severe acute respiratory syndrome coronavirus two (SARS-CoV-2) stems from a strong affinity for the cellular receptor angiotensin converting enzyme two (ACE2). We investigate the relationship between a patient's nasopharyngeal ACE2 transcription and secondary transmission within a series of concurrent hospital associated SARS-CoV-2 outbreaks in British Columbia, Canada. METHODS: Epidemiological case data from the outbreak investigations was merged with public health laboratory records and viral lineage calls, from whole genome sequencing, to reconstruct the concurrent outbreaks using infection tracing transmission network analysis. ACE2 transcription and RNA viral load were measured by quantitative real-time polymerase chain reaction. The transmission network was resolved to calculate the number of potential secondary cases. Bivariate and multivariable analyses using Poisson and Negative Binomial regression models was performed to estimate the association between ACE2 transcription the number of SARS-CoV-2 secondary cases. RESULTS: The infection tracing transmission network provided n = 76 potential transmission events across n = 103 cases. Bivariate comparisons found that on average ACE2 transcription did not differ between patients and healthcare workers (P = 0.86). High ACE2 transcription was observed in 98.6% of transmission events, either the primary or secondary case had above average ACE2. Multivariable analysis found that the association between ACE2 transcription (log2 fold-change) and the number of secondary transmission events differs between patients and healthcare workers. In health care workers Negative Binomial regression estimated that a one-unit change in ACE2 transcription decreases the number of secondary cases (ß = -0.132 (95%CI: -0.255 to -0.0181) adjusting for RNA viral load. Conversely, in patients a one-unit change in ACE2 transcription increases the number of secondary cases (ß = 0.187 (95% CI: 0.0101 to 0.370) adjusting for RNA viral load. Sensitivity analysis found no significant relationship between ACE2 and secondary transmission in health care workers and confirmed the positive association among patients. CONCLUSION: Our study suggests that ACE2 transcription has a positive association with SARS-CoV-2 secondary transmission in admitted inpatients, but not health care workers in concurrent hospital associated outbreaks, and it should be further investigated as a risk-factor for viral transmission.

Utilisation of and factors associated with non-COVID-19 healthcare services in public facilities amongst cross-border migrants in Thailand, 2019-2022.

BACKGROUND: It is believed that the COVID-19 pandemic might disrupt routine healthcare services. A vulnerable group such as cross-border migrants is of critical concern if the pandemic affects their service utilisation. In this study, we aim to explore the impact of COVID-19 and other related factors on non-COVID-19 service amongst cross-border migrants in Thailand. METHODS: We conducted an ecological time-series cross-sectional analysis using secondary data from 2019 to 2022, focusing on insured and non-insured migrants in a unit of a provincial monthly quarter. We obtained data on registered migrants from the Ministry of Labour and inpatient visits from the Ministry of Public Health (MOPH). Our analysis involved descriptive statistics and a random-effects negative binomial regression, considering variables such as COVID-19 cases, number of hospital beds, registered regions, and COVID-19 waves. We assessed inpatient utilisation number and rate as our primary outcomes. RESULTS: The admission numbers for insured and non-insured migrants in all regions increased 1.3-2.1 times after 2019 despite a decrease in the numbers of registered migrants. The utilisation of services for selected communicable and non-communicable diseases and obstetric conditions remained consistent throughout 2019-2022. The admission numbers and rates were not associated with an increase in COVID-19 incidence cases but significantly enlarged as time passed by compared to the pre-COVID-19 period (44.5-77.0% for insured migrants and 15.0-26.4% for non-insured migrants). Greater Bangkok saw the lowest admission rate amongst insured migrants, reflected by the incidence rate ratio of 5.7-27.5 relative to other regions. CONCLUSION: The admission numbers and rates for non-COVID-19 healthcare services remained stable regardless of COVID-19 incidence. The later pandemic waves (Delta and Omicron variants) were related to an increase in admission numbers and rates, possibly due to disruptions in outpatient care, leading to more severe cases seeking hospitalisation. Lower admission rates in Greater Bangkok may be linked to the fragmentation of the primary care network in major cities and the disintegration of service utilisation data between private facilities and the MOPH. Future research should explore migrant healthcare-seeking behaviour at an individual level, using both quantitative and qualitative methods for deeper insights.

[Dynamic Prediction of Recidivism in Violence in Community-Based Schizophrenia Spectrum Disorder Patients: A Joint Model]. / ç¤¾åŒºç²¾ç¥žåˆ†è£‚ç—‡è°±ç³»éšœç¢æ‚£è€ æš´åŠ›å†çŠ¯åŠ¨æ€é¢„æµ‹ï¼šä¸€ç§è”åˆæ¨¡åž‹.

Objective: To construct a model for predicting recidivism in violence in community-based schizophrenia spectrum disorder patients (SSDP) by adopting a joint modeling method. Methods: Based on the basic data on severe mental illness in Southwest China between January 2017 and June 2018, 4565 community-based SSDP with baseline violent behaviors were selected as the research subjects. We used a growth mixture model (GMM) to identify patterns of medication adherence and social functioning. We then fitted the joint model using a zero-inflated negative binomial regression model and compared it with traditional static models. Finally, we used a 10-fold training-test cross validation framework to evaluate the models' fitting and predictive performance. Results: A total of 157 patients (3.44%) experienced recidivism in violence. Medication compliance and social functioning were fitted into four patterns. In the counting model, age, marital status, educational attainment, economic status, historical types of violence, and medication compliance patterns were predictive factors for the frequency of recidivism of violence (P<0.05). In the zero-inflated model, age, adverse drug reactions, historical types of violence, medication compliance patterns, and social functioning patterns were predictive factors for the recidivism in violence (P<0.05). For the joint model, the average value of Akaike information criterion (AIC) for the train set was 776.5±9.4, the average value of root mean squared error (RMSE) for the testing set was 0.168±0.013, and the average value of mean absolute error (MAE) for the testing set was 0.131±0.018, which were all lower than those of the traditional static models. Conclusion: Joint modeling is an effective statistical strategy for identifying and processing dynamic variables, exhibiting better predictive performance than that of the traditional static models. It can provide new ideas for promoting the construction of comprehensive intervention systems.

Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT).

BACKGROUND: Fractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature. METHODS: Commonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT). RESULTS: The values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model. CONCLUSIONS: The negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses. TRIAL REGISTRATION: The PreFIT trial was registered as ISRCTN71002650.

Predictive modelling of Ross River virus using climate data in the Darling Downs.

Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 - June 2014 were used for model training. Model predictions were tested using data for July 2014 - June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.

Prediction of the COVID-19 transmission: a case study of Pakistan.

The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We've used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter.

BACKGROUND: The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. OBJECTIVE: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. METHODS: Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. RESULTS: A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. CONCLUSIONS: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.

Environmental characteristics and disparities in adult asthma in north central Texas urban counties.

OBJECTIVES: Disparities in asthma prevalence present a persistent challenge to public health. The complex nature of the issue requires studies through a wide range of lenses. To date, little research has examined associations between asthma and multiple social and environmental factors simultaneously. This study aims to fill the gap with a focus on the impacts of multiple environmental characteristics and social determinants of health on asthma. STUDY DESIGN: This study uses secondary analysis with data from a variety of sources to analyze the effects of environmental and social factors on adult asthma occurrence in North Central Texas. METHOD: Hospital records and demographic and environmental data for four urban counties in North Central Texas (Collin, Dallas, Denton, and Tarrant) come from the Dallas/Fort Worth Hospital Council Foundation, the US census, the North Central Texas Council of Governments, and the Railroad Commission of Texas. The data were integrated using ArcGIS. A hotspot analysis was performed to inspect the spatial patterns of hospital visits for asthma exacerbations in 2014. The impacts of multiple environmental characteristics and social determinants of health were modeled using negative binomial regression. RESULTS: The results revealed spatial clusters of adult asthma prevalence and disparities by race, class, and education. The occurrence of asthma exacerbations was positively associated with exposure to traffic-related air pollution, energy-related drilling activities, and older housing stock and negatively linked to green space. CONCLUSIONS: Associations between built environmental characteristics and asthma prevalence have implications for urban planners, healthcare professionals, and policy makers. Empirical evidence for the role of social determinants of health supports continuing efforts in policies and practices to improve education and reduce socio-economic inequities.

Impact of environmental factors on COVID-19 transmission: spatial variations in the world.

The COVID-19 pandemic caused enormous destruction to global health and the economy and has surged worldwide with colossal morbidity and mortality. The pattern of the COVID infection varies in diverse regions of the world based on the variations in the geographic environment. The multivariate generalized linear regression models: zero-inflated negative binomial regression, and the zero-inflated Poisson regression model, have been employed to determine the significant meteorological factors responsible for the spread of the pandemic in different continents. Asia experienced a high COVID-19 infection, and death was extreme in Europe. Relative humidity, air pressure, and wind speed are the salient factors significantly impacting the spread of COVID-19 in Africa. Death due to COVID-19 in Asia is influenced by air pressure, temperature, precipitation, and relative humidity. Air pressure and temperature substantially affect the spread of the pandemic in Europe.

There is a substantial variation of the impacts of environmental variables on the spread of the COVID-19 pandemic in different parts of the world.Temperature and air pressure significantly impact the number of infections and death due to COVID-19 in Europe.Relative humidity, temperature, sky clearness, and wind speed posed significant positive effects on COVID-19 in AfricaThe spread of COVID-19 infection and death is maximum in low temperatures.The confounding effect of the maximum number of meteorological factors minimizes the transmission of the pandemic.

[Statistical modeling of the fact and requests frequency for emergency medical care by primary care different age groups patients.]

The frequency of seeking emergency medical care (EC) can be mediated by the characteristics of the patient's social status, his health literacy (HL) but not only by the clinical signs of the disease, health status. The goal of the cross-sectional survey was to identify factors determining the frequency of applying for EC by the young-aged, middle-aged (18-59 years) and elderly (60-74 years) patients of the primary health organizations in the Arkhangelsk Region and the Komi Republic (North-West Russia). Logistic regression (LR) was used to identify factors mediating the fact of applying for EC; zero-inflated negative binomial regression (ZINB) - to identify factors mediating the frequency of appeals. The majority of elderly respondents in the Arkhangelsk Region (72,5%) and the Komi Republic (74,1%) applied for EC at least once during the calendar year; among the young-aged and middle-aged respondents - 45,3% and 52,1% respectively. In the group of young-aged and middle-aged respondents, a higher frequency of appeals for EC is mediated by the age, low self-esteem of the well-being and health status, a chronic disease(s) affecting daily well-being in anamnesis, a low level of HL; in the group of the elderly respondents - by the fact of absence of a spouse, low self-esteem of the well-being, a chronic disease(s) affecting daily well-being in anamnesis, low levels of HL respectively. The obtained results obtained can be used to identify the «risk group¼ of patients of the primary health organizations who have a higher probability of applying for EC, and to organize additional preventive work with the min primary health organizations.

Fragmented landscapes affect honey bee colony strength at diverse spatial scales in agroecological landscapes in Kenya.

Landscape fragmentation and habitat loss at multiple scales directly affect species abundance, diversity, and productivity. There is a paucity of information about the effect of the landscape structure and diversity on honey bee colony strength in Africa. Here, we present new insights into the relationship between landscape metrics such as patch size, shape, connectivity, composition, and configuration and honey bee (Apis mellifera) colony strength characteristics. Remote-sensing-based landscape variables were linked to honey bee colony strength variables in a typical highly fragmented smallholder agroecological region in Kenya. We examined colonies in six sites with varying degrees of land degradation during the period from 2017 to 2018. Landscape structure was first mapped using medium resolution bitemporal Sentinel-1 and Sentinel-2 satellite imagery with an optimized random forest model. The influence of the surrounding landscape matrix was then constrained to two buffer distances, i.e., 1 km representing the local foraging scale and 2.5 km representing the wider foraging scale around each investigated apiary and for each of the six sites. The results of zero-inflated negative binomial regression with mixed effects showed that lower complexity of patch geometries represented by fractal dimension and reduced proportions of croplands were most influential at local foraging scales (1 km) from the apiary. In addition, higher proportions of woody vegetation and hedges resulted in higher colony strength at longer distances from the apiary (2.5 km). Honey bees in moderately degraded landscapes demonstrated the most consistently strong colonies throughout the study period. Efforts towards improving beekeeper livelihoods, through higher hive productivity, should target moderately degraded and heterogeneous landscapes, which provide forage from diverse land covers.

A comparison of statistical methods for modeling count data with an application to hospital length of stay.

BACKGROUND: Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data. METHODS: Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database. RESULTS: Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors. CONCLUSIONS: Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion.

Prevalence trends and risk factors associated with HIV, syphilis, and hepatitis C virus among pregnant women in Southwest China, 2009-2018.

OBJECTIVE: This study investigated prevalence trends and identified the associated factors of HIV, syphilis and hepatitis C virus (HCV) among pregnant women in the Guangxi Zhuang Autonomous Region (Guangxi), Southwest China. METHODS: Serial cross-sectional surveys were performed annually among pregnant women in Guangxi from 2009 to 2018. Blood specimens were collected to test the prevalence of HIV, syphilis and HCV. Cochran-Armitage analysis was used to assess the trends of HIV, syphilis and HCV prevalence, as well as the sociodemographic and behavioural data. In this study, we used zero-inflated negative binomial (ZINB) regression models to identify factors associated with HIV, syphilis and HCV infection. RESULTS: A total of 23,879 pregnant women were included in the study. The prevalence of HIV, syphilis and HCV was 0.24%, 0.85% and 0.19%, respectively. There was a decrease in HIV prevalence from 0.54% to 0.10%, a decrease in HCV prevalence from 0.40% to 0.05% and a decrease in syphilis prevalence from 1.53% to 0.30%. The findings based on the ZINB model revealed that pregnant women who had a history of STI had significantly increased risks of HIV (OR 6.63; 95% CI 1.33-32.90) and syphilis (OR 9.06; 95% CI 3.85-21.30) infection, while pregnant women who were unmarried/widowed/divorced were more likely to have HIV (OR 2.81; 95% CI 1.20-6.54) and HCV (OR 58.12; 95% CI, 3.14-1076.99) infection. Furthermore, pregnant women whose husband had a history of STI (OR 5.62; 95% CI 1.24-25.38) or drug use (OR 7.36; 95% CI 1.25-43.43) showed an increased risk of HIV infection. CONCLUSIONS: There was a relatively low prevalence of HIV, syphilis and HCV among pregnant women. Although decreasing trends in HIV, syphilis and HCV infections were observed, effort is needed to promote STI testing in both premarital medical check-ups and antenatal care, especially targeting couples with a history of STI or drug use.

Historical trends in mortality from "older" vaccine-preventable diseases, Colombia: implications for elimination and control.

OBJECTIVE: This study aimed to describe the trends in mortality from eight vaccine-preventable diseases in Colombia in the last 40 years and their relationship with vaccination coverage. STUDY DESIGN: It is a population-based descriptive study. METHODS: The frequencies of deaths by decade, disease, sex, and the specific mortality rates by age group were calculated. Using a negative binomial regression model, the 10-year changes in mortality and their relationship with vaccination coverage were determined. RESULTS: The number of deaths and the adjusted rates decreased since 1989 in all diseases (incidence rate ratio <1 when compared with the 1979-1988 decade). Vaccination coverage below 90% is associated with an increase in mortality from diphtheria, measles, mumps, neonatal tetanus, and pertussis. CONCLUSION: Historical changes in mortality support the benefits of vaccination, but new efforts are required to sustain the elimination of diseases.

The effects of meteorological factors on the COVID-19 omicron variant in Bangladesh.

The COVID-19 omicron variant is exceptionally complicated and uncertain due to its rapid transmission and volume of infections. This study examines the impact of climatic factors on daily confirmed cases of COVID-19 omicron variant in Bangladesh. The secondary data of daily confirmed cases from 1 January 2022, to 31 March 2022, of eight distinct geographic divisions have been used for the current study. The multivariate generalized linear negative binomial regression model was applied to determine the effects of climatic factors on omicron transmission. The model revealed that the maximum temperature (Odds: 0.67, p < 0.05), sky clearness (Odds: 0.05, p < 0.05), wind speed (Odds: 0.76, p < 0.05), relative humidity (Odds: 1.02, p < 0.05), and air pressure (Odds: 0.27, p < 0.05) significantly impacted COVID-19 omicron transmission in Bangladesh. The study's findings can assist the concerned authorities and decision-makers take necessary measures to control the spread of omicron cases in Bangladesh.

Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande, MS, Brazil.

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

Impact of environmental factors on the spread of dengue fever in Sri Lanka.

Dengue fever is a mosquito-borne viral disease caused by the dengue virus of the Flaviviridae family and is responsible for colossal health and economic burden worldwide. This study aimed to investigate the effect of environmental, seasonal, and spatial variations on the spread of dengue fever in Sri Lanka. The study used secondary data of monthly dengue infection and the monthly average of environmental parameters of 26 Sri Lankan regions from January 2015 to December 2019. Besides the descriptive measurements, Kendall's tau_b, Spearman's rho, and Kruskal-Wallis H test have been performed as bivariate analyses. The multivariate generalized linear negative binomial regression model was applied to determine the impacts of meteorological factors on dengue transmission. The aggregate negative binomial regression model disclosed that precipitation (odds ratio: 0.97, p < 0.05), humidity (odds ratio: 1.05, p < 0.01), and air pressure (odds ratio: 1.46, p < 0.01) were significantly influenced the spread of dengue fever in Sri Lanka. The bioclimatic zone is the vital factor that substantially affects the dengue infection, and the wet zone (odds ratio: 6.41, p < 0.05) was more at-risk than the dry zone. The climate season significantly influenced dengue fever transmission, and a higher infection rate was found (odds ratio: 1.46, p < 0.01) in the northeast monsoon season. The findings of this study facilitate policymakers to improve the existing dengue control strategies focusing on the meteorological condition in the local as well as global perspectives.

Spatial Modeling of leprosy disease in east java province with spatially varying regression coefficients models.

Indonesia ranks third with the most leprosy cases globally. East Java is the province that has the highest leprosy cases. The Provincial Government socialized the East Java Leprosy Eradication Program, which targets a maximum of one leprosy case per 10,000 residents. We propose spatially varying regression coefficients models to evaluate the effects of risk factors on of leprosy cases in East Java, use Geographically Weighted Generalized Poisson Regression and Geographically Weighted Negative Binomial Regression (GWNBR) models. The best models GWNBR categorize municipalities into six groups based on variables that have a significant impact on leprosy cases. The percentage of households with access to adequate sanitation is a significant factor in determining leprosy cases in all municipalities in East Java. We can conclude that clean and healthy living behavior, health facilities, and health workers significantly affect the number of leprosy cases in East Java.