Conditional Autoregressive Model Research Articles

Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil.

Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country. This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.

Spatial Analysis of COVID-19 Incidence and the Sociodemographic Context in Brazil

Background: In the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil’s municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic’s spread in the country.Methods: This is an analytical ecological study using data from various sources. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).Findings: The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.Discussion: Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.Funding Statement: None to declare. Declaration of Interests: None to declare.

Effects of Climatic Factors on Dengue Incidence: A Comparison of Bayesian Spatio-Temporal Models

Considering only the spatial component of diseases can identify areas with reduced or elevated risk, but not capture anything about temporal variation of risk which could be more or equally crucial. Hence, both spatial and temporal components of diseases need to be considered. Bayesian methods are useful due to the ease of specifying additional information, including temporal or spatial structure, through prior distributions. Here, we examine a range of different Bayesian spatio-temporal models available using CARBayes. Combinations of model formulations and climatic covariates were compared using goodness-of-fit measures, such as Watanabe Akaike Information Criterion (WAIC). Comparisons were made in the context of a substantive case study, namely monthly dengue fever incidence from January 2013 to December 2017 and climatic covariates in 14 geographic areas of Makassar, Indonesia. A spatio-temporal conditional autoregressive adaptive model combining rainfall and average humidity provided the most suitable model.

Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability

BackgroundPrevious studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes. ObjectivesWe examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk. MethodsA Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors. ResultsWe found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation. ConclusionsSpatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.

ITERATIONS OF DEPENDENT RANDOM MAPS AND EXOGENEITY IN NONLINEAR DYNAMICS

We discuss the existence and uniqueness of stationary and ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into the dynamic. To this end, we consider the convergence of the backward iterations of dependent random maps. In particular, we give a new result when the classical condition of contraction on average is replaced with a contraction in conditional expectation. Under some conditions, we also discuss the dependence properties of these processes using the functional dependence measure of Wu (2005, Proceedings of the National Academy of Sciences 102, 14150–14154) that delivers a central limit theorem giving a wide range of applications. Our results are illustrated with conditional heteroscedastic autoregressive nonlinear models, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes, count time series, binary choice models, and categorical time series for which we provide many extensions of existing results.

Leroux Spatial Model for Mapping the Relative Risk of Dengue Fever in Makassar

One of the health problems in Makassar city is dengue fever (DF) disease. To find out areas in Makassar that are at high risk of spreading DF, a relative risk analysis can be done. Bayesian Conditional Autoregressive (CAR) is a model used in disease mapping by taking into account the smoothing of the estimated relative risk and using spatial information to obtain a better estimate of relative risk. The Leroux model is a prior used in the CAR model for estimating the relative risk of DF in Makassar city. The relative risk estimation is used for geographic mapping of DF. In this model random and spatial variables are used. The results of this study indicate that DF medium relative risk occurred in some sub-districts of Makassar city. It indicates that the population in these areas susceptible and more likely to be infected with DF disease. This gives a clue so the Health Department of Makassar city can provide more intensive treatment to medium relative risk sub-districts so that the spread of DF disease in Makassar can be overcome.

Dengue and SARS-CoV-2 Co-Circulation Early Warning according to Climate Variations in Cuba

Dengue and Severe Acute Respiratory Syndromes Coronavirus 2 (SARS-CoV-2) co-circulation is a reality resulting to be more dangerous in regions that are endemic for arboviruses. Circulation of both viruses continue spreading, mainly in tropical settings causing a high impact on the health systems of the countries. Therefore, forecasting the circulation of SARS-CoV-2 virus and dengue from the climatic variability a temporal and spatial scale allows to perfect the warning system and provides a tool for decision makers in the health system to take the necessary control measures. Methods: Ecological study with retrospective-prospective analysis of the series of the SARS- CoV-2 viruses, dengue, as well as, Aedes focus with their index and the climatic anomalies described by the complex climatic Index of Bulto (BI1, r, t). Interpolation method for the spatial structure with continuous information of 1200 nodes (Raster format) was generated. Kriging method combined with the method of the inverse distance (IDM) was implemented with a resolution of 10 km2 . The Multivariate Moran Index was used to determine the spatial correlation. For the prediction, the Simultaneous Autoregressive Models and the Spatial Autoregressive Conditional Models were used. Temporal modeling was carried out by using the Heteroscedastic Conditional Autoregressive and Autoregressive Models, both with exogenous variables. To obtain the co-circulation risk forecast maps, a stratification of the circulation of both viruses is carried out and map algebra is applied. Results: The moments of greatest risk Dengue and SARS-CoV-2 co-circulation are the months corresponding to the second quarter of the rainy period August-October with very humid conditions, very high temperatures, high volume of precipitation, high cloudiness, being potential predictors. All these results led to the creation of the dengue alert system and co-circulation with SARS-CoV-2 based on the observation and forecast of climatic factors. The areas with major viral co-circulation were the Central-Eastern region. Conclusions: It is evident that the climate is an important determinant for the health sector, and in particular to understand the Dengue and SARS-CoV-2 behavior and co-circulation, which strengthens the active health surveillance system.

On the computational Bayesian survival spatial dengue hemorrhagic fever (DHF) modeling with double-exponential CAR frailty

In statistics, there are many types of data. Some data carry information about the location where observations occur, so that they can have a spatial effect. Dengue hemorrhagic fever (DHF) data which is easily transmitted, will be consequently has a spatial effect on its patient survival. In this study, we included DHF patient recovery time as a response variable, and several other variables as covariates considered to influence the patient’s recovery time. Our aim in this study is to model how these variables affect the recovery rate for DHF patients with the accompanying patient residence as the spatial effects. Survival analysis is the best method for modeling the recovery rate for DHF patients. A conditional autoregressive (CAR) model is given to explain the relationship between adjacent locations, which is not explained in the general survival analysis. Several researchers have used the Cox model coupled with the Normal CAR. In this study, we used the Cox model using Normal CAR and compared it with the Double-Exponential (DE) CAR. To estimate the regression parameters of the Cox model, we used the Stan software. The advantage of Stan compared to the other Bayesian software such as BUGS and JAGS is the creativity of the researcher in writing the distribution as user-defined, as well as writing the CAR model in the Stan. Based on the WAIC value, modeling the DHF data using the Cox model coupled with the DE CAR is better than coupled with the Normal CAR. Based on the best model, variables that affect the recovery rate of DHF patients are age, the high schools in last education, unemployed in the type of occupations, the stadium II in severity level, pulse, temperature, and leukocytes.

Conditional Threshold Autoregression (CoTAR)

We propose a new time series model where the threshold is specified as an empirical quantile of recent observations of a threshold variable. The resulting conditional threshold traces the fluctuation of the threshold variable, which can enhance the fit and interpretation of the model. In the proposed conditional threshold autoregressive (CoTAR) model, the existence of threshold effects can be tested by wild-bootstrap tests which incorporate all possible values of nuisance parameters. The estimation and hypothesis testing of the CoTAR model satisfy desired statistical properties in both large and small samples. We fit the CoTAR model to new confirmed COVID-19 cases in the U.S. and Japan. Significant conditional threshold effects are detected for both countries, and the implied persistence structures are consistent with the fact that the number of new confirmed cases in the U.S. is larger than in Japan.

Aggregate-level Lead Exposure and Child Maltreatment.

The purpose of this study was to examine the possible ecological association between aggregate blood lead levels (BLL) and rates of child maltreatment. To this end, we employed an ecologic study design, analyzing results from 59,645 child BLL tests between the years 1996 and 2007, and 6,640 substantiated maltreatment investigations from 2006 to 2016 in a large Midwest city. Separate Bayesian spatial Poisson conditional autoregressive (CAR) and Bayesian spatial zero-inflated Poisson CAR models were used to predict the occurrence of maltreatment.Bivariate results showed that aggregate rates of maltreatment increased as aggregate BLL increased. Multivariate results showed that medium-exposure BLL census tracts (OR = 1.38) and high-exposure BLL tracts (OR = 1.38) had increased odds of substantiated investigations for any maltreatment compared to low BLL census tracts even after controlling for crime rates, age of the housing stock, and concentrated disadvantage. Our findings, considered with prior research, continue to reveal a confluence of deleterious outcomes in areas where exposure to lead seems elevated. In this case, child maltreatment also appears to represent a macro-level correlate of aggregate lead exposure. Yet our results preclude any causal inference, and further research on the intersection of child maltreatment with environmental toxins is needed to determine if contaminant abatement should be considered as a possible maltreatment prevention strategy.

Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models

In this article, we consider a quasi‐maximum likelihood (QML) estimation of conditional autoregressive Wishart models, which generalize the conditional autoregressive Wishart models by not restricting the conditional distribution of covariances to follow the Wishart distribution. Strong consistency is established under the existence of the expectation of the log of the determinant. Sufficient conditions for asymptotic normality of the QML estimator are derived. Monte Carlo experiments show an inefficiency caused by using non‐Wishart distributions, which are negligible for the sample size T = 500. We use the daily covariance matrix of the returns of the Nikkei 225 index and its futures for the QML estimation of the conditional autoregressive Wishart model. The results indicate its appropriateness for the QML estimation.

Improving models of wild boar hunting yield distribution: new insights for predictions at fine spatial resolution

ENETWILD consortium has developed methodologies for modelling wild boar abundance distribution based on hunting yield (HY) data. Although the methodologies reached an acceptable reliability, when models were downscaled to higher spatial resolution the predictions of absolute numbers of hunted animals tended to overprediction. Some important issues such as HY-surface relationship and the spatial autocorrelation of HY data or the accuracy of downscaled predictions were not fully addressed yet due to the complexity of dealing with huge datasets at a European scale. In this report we (i) explored the use of hunted wild boar densities (numbers of hunted wild boar relative to surface) instead of raw counts (numbers of hunted animals) as response variable, and (ii) introduced intrinsic Conditional Auto-Regressive models (iCAR) taking into account spatial autocorrelation. Using simulations and actual wild boar data, these new actions were aimed to produce high resolution predictions (2x2 km grid) with higher accuracy. We assessed model fitting in two different regions in Europe with high quality resolution HY data: Aragón autonomous region (North East Spain, belonging to South Bioregion as defined by ENETWILD) and the whole country of Slovenia (East Bioregion). We found that the marked overprediction, as observed in previous reports when models were downscaled, was now controlled by using hunted wild boar densities as response variable. Additionally, higher accuracy in model predictions was reached when iCAR approach was used to control for spatial autocorrelation. This high accuracy was maintained even when high resolution predictions were aggregated and compared to actual wild boar HY. These approaches should be considered in future models and represent an important step forward to model the distribution of wild boar abundance and other wildlife at high resolution over Europe.

PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL

In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.

An integrated framework for visualizing and forecasting realized covariance matrices

This paper proposes an integrated framework for visualizing and forecasting realized covariance matrices to enable the efficient construction and prediction of an optimal portfolio. Multivariate realized kernels are typically derived from intra-day high-frequency data, and are then used to estimate the realized covariance matrix via the graphical lasso algorithm. To forecast the realized covariances, we employ the conditional autoregressive Wishart model and its variants. Finally, we compute the Stein loss function and execute the model-confidence-set procedure to obtain the best model for optimal portfolio selection.

Spatio-temporal analysis of flood data from South Carolina

To investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

Mapping of anaemia prevalence among pregnant women in Kenya (2016\u20132019)

BackgroundReducing the burden of anaemia is a critical global health priority that could improve maternal outcomes amongst pregnant women and their neonates. As more counties in Kenya commit to universal health coverage, there is a growing need for optimal allocation of the limited resources to sustain the gains achieved with the devolution of healthcare services. This study aimed to describe the spatio-temporal patterns of maternal anaemia prevalence in Kenya from 2016 to 2019.MethodsQuarterly reported sub-county level maternal anaemia cases from January 2016 – December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical negative binomial spatio-temporal conditional autoregressive (CAR) model was used to estimate maternal anaemia prevalence by sub-county and quarter. Spatial and temporal correlations were considered by assuming a conditional autoregressive and a first-order autoregressive process on sub-county and seasonal specific random effects, respectively.ResultsThe overall estimated number of pregnant women with anaemia increased by 90.1% (95% uncertainty interval [95% UI], 89.9–90.2) from 155,539 cases in 2016 to 295,642 cases 2019. Based on the WHO classification criteria, the proportion of sub-counties with normal prevalence decreased from 28.0% (95% UI, 25.4–30.7) in 2016 to 5.4% (95% UI, 4.1–6.7) in 2019, whereas moderate anaemia prevalence increased from 16.8% (95% UI, 14.7–19.1) in 2016 to 30.1% (95% UI, 27.5–32.8) in 2019 and severe anaemia prevalence increased from 7.0% (95% UI, 5.6–8.6) in 2016 to 16.6% (95% UI, 14.5–18.9) in 2019. Overall, 45.1% (95% UI: 45.0–45.2) of the estimated cases were in malaria-endemic sub-counties, with the coastal endemic zone having the highest proportion 72.8% (95% UI: 68.3–77.4) of sub-counties with severe prevalence.ConclusionAs the number of women of reproductive age continues to grow in Kenya, the use of routinely collected data for accurate mapping of poor maternal outcomes remains an integral component of a functional maternal health strategy. By unmasking the sub-county disparities often concealed by national and county estimates, our study findings reiterate the importance of maternal anaemia prevalence as a metric for estimating malaria burden and offers compelling policy implications for achieving national nutritional targets.

Association between residential green space during childhood and adult earnings: A longitudinal study of environmental determinants of the American Dream across ten United States cities

Background: Growing up in poverty is associated with poor health, and the American Dream of upward mobility is becoming an illusion for many low-income children. But nearby green space can support academic achievement, creativity, and emotional regulation, and these traits might help children rise out of poverty.Objectives: To examine the relationship between recent incomes of children born into poverty in the ten largest U.S. cities and densities of residential green space during childhood.Methods: We calculate park proximity, park acreage, new park development, and NDVI greenness for 1980-1990 from Landsat and Trust for Public Land data. We obtain the 2014 income for children born between 1978 and 1982 into families in poverty from The Opportunity Atlas cohort, aggregated at the tract level (n = 5849).Results: Conditional autoregressive (CAR) models of tracts show statistically significant associations between income rank and above-average levels of greenness but not between income rank and park measures, adjusting for individual and neighborhood confounders and spatial autocorrelation. We estimate that, over a 30-year career, children growing up in tracts with the most vegetative cover will earn cumulatively $28,000 more than children growing up in tracts with the least cover, on average. Tracts with lower than average levels of precipitation, higher disadvantage, higher population density, or higher annual temperatures do not show beneficial effects of green space.Conclusions: Greenness may be weakly associated with children rising out of poverty in wetter, cooler, less-dense, more advantaged census tracts.

Spatial distribution of tuberculosis incidence in Los Angeles County

BackgroundIn Los Angeles County, the tuberculosis (TB) disease incidence rate is seven times higher among non-U.S.-born persons than U.S.-born persons and varies by country of birth. But translating these findings into public health action requires more granular information, especially considering that Los Angeles County is more than 4000 mile2. Local public health authorities may benefit from data on which areas of the county are most affected, yet these data remain largely unreported in part because of limitations of sparse data. We aimed to describe the spatial distribution of TB disease incidence in Los Angeles County while addressing challenges arising from sparse data and accounting for known cofactors.MethodsData on 5447 TB cases from Los Angeles County were combined with stratified population estimates available from the 2005–2011 Public Use Microdata Survey. TB disease incidence rates stratified by country of birth and Public Use Microdata Area were calculated and spatial smoothing was applied using a conditional autoregressive model. We used Bayesian Poisson models to investigate spatial patterns adjusting for age, sex, country of birth and years since initial arrival in the U.S.ResultsThere were notable differences in the crude and spatially-smoothed maps of TB disease rates for high-risk subgroups, namely persons born in Mexico, Vietnam or the Philippines. Spatially-smoothed maps showed areas of high incidence in downtown Los Angeles and surrounding areas for persons born in the Philippines or Vietnam. Areas of high incidence were more dispersed for persons born in Mexico. Adjusted models suggested that the spatial distribution of TB disease could not be fully explained using age, sex, country of birth and years since initial arrival.ConclusionsThis study highlights areas of high TB incidence within Los Angeles County both for U.S.-born cases and for cases born in Mexico, Vietnam or the Philippines. It also highlights areas that had high incidence rates even when accounting for non-spatial error and country of birth, age, sex, and years since initial arrival in the U.S. Information on spatial distribution provided here complements other descriptions of local disease burden and may help focus ongoing efforts to scale up testing for TB infection and treatment among high-risk subgroups.

Environmental and climatic risk factors for cystic and alveolar echinococcosis in Kyrgyzstan

Abstract Cystic and alveolar echinococcosis-CE and AE-are parasitic zoonoses caused by tapeworms Echinococcus granulosus and E. multilocularis. Their lifecycle is associated with particular environmental and climatic conditions, which have been identified as risk factors for CE and AE transmission. In this study, we assessed potential environmental and climatic risk factors for CE and AE in Kyrgyzstan where both diseases are endemic. We previously estimated CE and AE surgical incidence and standardised incidence ratio (SIR) at the local community level in Kyrgyzstan for the period 2014-2016. We obtained 190 geospatial covariates on potential environmental and climatic risk factors for the two diseases from remotely sensed datasets. To account for the prolonged latency of CE and AE, we used covariates for different years (i.e. 2000, 2005 and 2010) and for an extended period of 30 years (i.e. monthly mean). We summarised the different covariates at the local community level and assessed correlations with the SIR of CE and AE using the Spearman's rank correlation test. We also computed the variance inflation factor (VIF) to assess multicollinearity between covariates. We then fitted three types of conditional autoregressive (CAR) models for each disease, namely an independent, a convolution, and a so-called Leroux model. We used covariates exhibiting significant correlation with the SIR of CE or AE and no multicollinearity as explanatory variables, and case counts as response variables. We selected the best CAR model and further refined the covariate selection using the Deviance Information Criterion (DIC). Convolution models exhibited the best fit in terms of DIC for both diseases. None of the selected environmental variables exhibited significant effect on the number of CE cases, whilst there was a significative negative effect of mean annual temperature in 2005 on AE cases. These preliminary findings might inform targeted prevention and control in high-risk areas. Key messages We assessed environmental and climatic risk factors for cystic and alveolar echinococcosis in Kyrgyzstan. Our preliminary findings suggest that alveolar echinococcosis risk in Kyrgyzstan is associated with climatic factors such as 10-year lag mean annual temperature.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia.

.Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level—where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space–time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.