Effects of extreme precipitation to the distribution of infectious diseases in Taiwan, 1994-2008

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh

BACKGROUND

A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change.

OBJECTIVES

Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change.

METHODS

We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters.

RESULTS

Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null.

DISCUSSION

Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

Joint extremes in precipitation and infectious disease in the USA: A bivariate POT study

Mounting heavy precipitation events (HPEs) caused by the climate change have drawn wide attention. Increased incidences of infectious diseases are known as the common following health impact, while little has been studied about the extremal relationship in between. Therefore, this study aims to investigate the joint extremes of precipitation and infectious disease mortality rate in the USA, using publicly accessible data from the National Centers for Environmental Information and the Centers for Disease Control and Prevention. The study reveals the positive association between heavy precipitations and infectious diseases with slight national and regional differences using multivariate Peaks-Over-Threshold modelling. The strength of extremal dependence is measured by the extreme parameter α from a logistic dependence model in multivariate extreme value theory. The Midwestern USA shows an excessive impact of HPEs on infectious disease mortality (α = 0.7524 ), while the other regions show similar extremal dependence strength with the national one (α values all approximate 0.77). The study also discovered spatial disparities in the extremal dependences for five sub-categories of infectious diseases in each census region, among which mycoses show the strongest extremal dependence with precipitation in almost all regions. These spatial differences of extremal dependence may be attributed to geographic, social-economic factors and the self-inherited characteristics of certain diseases. The findings are expected to assist in developing strategies counteracting extreme risks resulting from weather events and health issues as well. The cutting-edge multivariate Peaks-Over-Threshold (POT) approach employed herein also shows promise for a wide range of extreme risk assessment topics.

The seroincidence of childhood Shigella sonnei infection in Ho Chi Minh City, Vietnam

BACKGROUND

Shigella sonnei is a pathogen of growing global importance as a cause of diarrhoeal illness in childhood, particularly in transitional low-middle income countries (LMICs). Here, we sought to determine the incidence of childhood exposure to S. sonnei infection in a contemporary transitional LMIC population, where it represents the dominant Shigella species.

METHODS

Participants were enrolled between the age of 12-36 months between June and December 2014. Baseline characteristics were obtained through standardized electronic questionnaires, and serum samples were collected at 6-month intervals over two years of follow-up. IgG antibody against S. sonnei O-antigen (anti-O) was measured using an enzyme-linked immunosorbent assay (ELISA). A four-fold increase in ELISA units (EU) with convalescent IgG titre >10.3 EU was taken as evidence of seroconversion between timepoints.

RESULTS

A total of 3,498 serum samples were collected from 748 participants; 3,170 from the 634 participants that completed follow-up. Measures of anti-O IgG varied significantly by calendar month (p = 0.03). Estimated S. sonnei seroincidence was 21,451 infections per 100,000 population per year (95% CI 19,307-23,834), with peak incidence occurring at 12-18 months of age. Three baseline factors were independently associated with the likelihood of seroconversion; ever having breastfed (aOR 2.54, CI 1.22-5.26), history of prior hospital admission (aOR 0.57, CI 0.34-0.95), and use of a toilet spray-wash in the household (aOR 0.42, CI 0.20-0.89).

CONCLUSIONS

Incidence of S. sonnei exposure in Ho Chi Minh City is substantial, with significant reduction in the likelihood of exposure as age increases beyond 2 years.

"Measuring the Environmental Context of Child Growth in Burkina Faso"

Child growth failure, as indicated by low height-for-age z-scores (HAZ), is an important metric of health, social inequality, and food insecurity. Understanding the environmental pathways to this outcome can provide insight into how to prevent it. While other studies have examined the environmental determinants of HAZ, there is no agreed upon best-practices approach to measure the environmental context of this outcome. From this literature, we derive a large set of potential environmental predictors and specifications including temperature and precipitation levels, anomalies, and counts as well as vegetation anomalies and trends, which we include using linear, nonlinear, and interactive specifications. We compare these measures and specifications using four rounds of DHS survey data from Burkina Faso and a large set of fixed effects regression models, focusing on exposures from the time of conception through the second year of life and relying on joint hypothesis tests and goodness-of-fit measures to determine which approach best explains HAZ. Our analysis reveals that nonlinear and interactive transformations of climate anomalies, as opposed to climate levels or vegetation indices, provide the best explanation of child growth failure. These results underline the complex and nonlinear pathways through which climate change affects child health and should motivate climate-health researchers to more broadly adopt measures and specifications that capture these pathways.

The impact of temperature and precipitation on all-infectious-, bacterial-, and viral-diarrheal disease in Taiwan

BACKGROUND

The ongoing climate change will elevate the incidence of diarrheal in 2030-2050 in Asia, including Taiwan. This study investigated associations between meteorological factors (temperature, precipitation) and burden of age-cause-specific diarrheal diseases in six regions of Taiwan using 13 years of (2004-2016) population-based data.

METHODS

Weekly cause-specific diarrheal and meteorological data were obtained from 2004 to 2016. We used distributed lag non-linear model to assess age (under five, all age) and cause-specific (viral, bacterial) diarrheal disease burden associated with extreme high (99th percentile) and low (5th percentile) of climate variables up to lag 8 weeks in six regions of Taiwan. Random-effects meta-analysis was used to pool these region-specific estimates.

RESULTS

Extreme low temperature (15.30 °C) was associated with risks of all-infectious and viral diarrhea, with the highest risk for all-infectious diarrheal found at lag 8 weeks among all age [Relative Risk (RR): 1.44; 95 % Confidence Interval (95 % CI): 1.24-1.67]. The highest risk of viral diarrheal infection was observed at lag 2 weeks regardless the age. Extreme high temperature (30.18 °C) was associated with risk of bacterial diarrheal among all age (RR: 1.07; 95 % CI: 1.02-1.13) at lag 8 weeks. Likewise, extreme high precipitation (290 mm) was associated with all infectious diarrheal, with the highest risk observed for bacterial diarrheal among population under five years (RR: 2.77; 95 % CI: 1.60-4.79) at lag 8 weeks. Extreme low precipitation (0 mm) was associated with viral diarrheal in all age at lag 1 week (RR: 1.08; 95 % CI: 1.01-1.15)].

CONCLUSION

In Taiwan, extreme low temperature is associated with an increased burden of viral diarrheal, while extreme high temperature and precipitation elevated burden of bacterial diarrheal. This distinction in cause-specific and climate-hazard specific diarrheal disease burden underscore the importance of incorporating differences in public health preparedness measures designed to enhance community resilience against climate change.

Arboviruses in Mammals in the Neotropics: A Systematic Review to Strengthen Epidemiological Monitoring Strategies and Conservation Medicine

Arthropod-borne viruses (arboviruses) are a diverse group of ribonucleic acid (RNA) viruses, with the exception of African swine fever virus, that are transmitted by hematophagous arthropods to a vertebrate host. They are the important cause of many diseases due to their ability to spread in different environments and their diversity of vectors. Currently, there is no information on the geographical distribution of the diseases because the routes of transmission and the mammals (wild or domestic) that act as potential hosts are poorly documented or unknown. We conducted a systematic review from 1967 to 2021 to identify the diversity of arboviruses, the areas, and taxonomic groups that have been monitored, the prevalence of positive records, and the associated risk factors. We identified forty-three arboviruses in nine mammalian orders distributed in eleven countries. In Brazil, the order primates harbor the highest number of arbovirus records. The three most recorded arboviruses were Venezuelan equine encephalitis, Saint Louis encephalitis and West Nile virus. Serum is the most used sample to obtain arbovirus records. Deforestation is identified as the main risk factor for arbovirus transmission between different species and environments (an odds ratio of 1.46 with a 95% confidence interval: 1.34-1.59). The results show an increase in the sampling effort over the years in the neotropical region. Despite the importance of arboviruses for public health, little is known about the interaction of arboviruses, their hosts, and vectors, as some countries and mammalian orders have not yet been monitored. Long-term and constant monitoring allows focusing research on the analysis of the interrelationships and characteristics of each component animal, human, and their environment to understand the dynamics of the diseases and guide epidemiological surveillance and vector control programs. The biodiversity of the Neotropics should be considered to support epidemiological monitoring strategies.

Effects of Ambient Temperature, Relative Humidity, and Precipitation on Diarrhea Incidence in Surabaya

BACKGROUND

Diarrhea remains a common infectious disease caused by various risk factors in developing countries. This study investigated the incidence rate and temporal associations between diarrhea and meteorological determinants in five regions of Surabaya, Indonesia.

METHOD

Monthly diarrhea records from local governmental health facilities in Surabaya and monthly means of weather variables, including average temperature, precipitation, and relative humidity from Meteorology, Climatology, and Geophysical Agency were collected from January 2018 to September 2020. The generalized additive model was employed to quantify the time lag association between diarrhea risk and extremely low (5th percentile) and high (95th percentile) monthly weather variations in the north, central, west, south, and east regions of Surabaya (lag of 0-2 months).

RESULT

The average incidence rate for diarrhea was 11.4 per 100,000 during the study period, with a higher incidence during rainy season (November to March) and in East Surabaya. This study showed that the weather condition with the lowest diarrhea risks varied with the region. The diarrhea risks were associated with extremely low and high temperatures, with the highest RR of 5.39 (95% CI 4.61, 6.17) in the east region, with 1 month of lag time following the extreme temperatures. Extremely low relative humidity increased the diarrhea risks in some regions of Surabaya, with the highest risk in the west region at lag 0 (RR = 2.13 (95% CI 1.79, 2.47)). Extremely high precipitation significantly affects the risk of diarrhea in the central region, at 0 months of lag time, with an RR of 3.05 (95% CI 2.09, 4.01).

CONCLUSION

This study identified a high incidence of diarrhea in the rainy season and in the deficient developed regions of Surabaya, providing evidence that weather magnifies the adverse effects of inadequate environmental sanitation. This study suggests the local environmental and health sectors codevelop a weather-based early warning system and improve local sanitation practices as prevention measures in response to increasing risks of infectious diseases.

The impact of heavy precipitation and its impact modifiers on shigellosis occurrence during typhoon season in Taiwan: A case-crossover design

Because of climate change, heavy precipitation is likely to become frequent and intense, thereby increasing the risk of shigellosis occurrence. However, few studies examined the impact of heavy precipitation on shigellosis and its impact modifiers in developed countries. This study aims to analyze the association between heavy precipitation and shigellosis in Taiwan, and to identify the vulnerable population and impact modifiers. We adopted a case-crossover design, and used conditional logistic regression to estimate odds ratios (ORs) for shigellosis occurrence. Information were collected on the daily shigellosis cases, precipitation, temperature, and typhoons from 1994 to 2015, and yearly data of medical resources and environmental factors were obtained at the city level from 1998 to 2015. Stratification analyses were performed by age, sex, medical resource, and environmental factors. We discovered that heavy precipitation ≥80 mm/day considerably increased the risk of shigellosis occurrence. The ORs of heavy rain (80 to <200 mm/day) were 2.08-2.26 at lags 0-1. The ORs of extremely heavy rain (≥200 mm/day) increased to 2.17-4.73 at lags 5-8. Moreover, the effect of heavy precipitation was greater under high temperature condition (≥23.6 °C). Adults were more susceptible to heavy-precipitation-associated shigellosis, especially the elderly. Males experienced marginally higher effects than females did. Moreover, cities with more medical resources and forest cover and higher percentage of completed storm sewers had lower effects; however, dense population and higher pig density were the risk factors. Although the high water-supply penetration rate did not decrease Shigella infection after heavy precipitation, it did lower the risk of typhoon-related shigellosis. In conclusion, hot temperature could enhance the impact of heavy precipitation on shigellosis. Public health interventions should be introduced according to the lag period after heavy precipitation, particularly in areas with high population density, proportion of elderly people, and pig density. The improvement of medical resources and tree cover as well as the construction of storm sewers and piped water systems might be mitigation measures that can be considered.

ConvMOS: climate model output statistics with deep learning

Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.

Pathways linking climate change and HIV/AIDS: An updated conceptual framework and implications for the Philippines

As we commemorate the 40th anniversary of the discovery of Human Immunodeficiency Virus (HIV) while fighting the ongoing COVID-19 pandemic, another global crisis - climate change - is threatening the progress achieved so far in the global fight against HIV/AIDS. The climate emergency is anticipated to generate dire health consequences worldwide in the coming decades. While the pathways that link climate change and different disease areas are better understood, the connection between climate change and HIV/AIDS is still yet to be recognized both in research and practice. In this review, we update one of the frameworks on the HIV-climate nexus described in earlier literature. Four major pathways have been identified: extreme weather events; sea level rise; changes in precipitation and temperature; and increased air pollution. These pathways impact the spectrum of HIV/AIDS-related outcomes through changes in social systems, healthcare disruption, and other climate-sensitive diseases, influenced by the social determinants of health. We also reflect on the significance of this updated framework for the Philippines, a country that is both highly vulnerable to the climate crisis and facing a rising HIV/AIDS epidemic. The framework can aid countries like the Philippines in filling gaps in research, policy, and program design to mount climate-adaptive HIV/AIDS responses. The HIV/AIDS and climate justice movements must also join forces in calling for accelerated worldwide decline in greenhouse gas emissions from all sectors to stabilize the global climate - this will benefit not just people affected by HIV/AIDS but everyone.

A Review of Historical Changes of Tropical and Extra-Tropical Cyclones: A Comparative Analysis of the United States, Europe, and Asia

Tropical cyclones are highly destructive weather systems, especially in coastal areas. Tropical cyclones with maximum sustained winds exceeding 74 mph (≈119 kph) are classified as typhoons in the Northwest Pacific, whilst the term ‘hurricanes’ applies to other regions. This study aims to investigate the general characteristics of the most devastating and catastrophic tropical cyclones in the USA Europe, and Asia. To achieve the study objectives, the three most devastating typical tropical cyclones in each region were selected. The tropical cyclones were examined based on various features, such as the number of deaths, minimum pressure, highest wind speed, total financial losses, and frequency per year. In contrast to Europe and Asia, the USA has recorded the highest number of catastrophic tropical cyclones. The damage induced by hurricanes Katrina, Harvey, and Maria in the USA totalled approximately USD USD 380 billion. In addition, the present research highlights the demand to improve the public attitude and behaviour toward the impact of climate change along with the enhancement of climate change alleviation strategies. The number of intense tropical cyclones is expected to rise, and the tropical cyclone-related precipitation rate is expected to increase in warmer-climate areas. Stakeholders and industrial practitioners may use the research findings to design resilience and adaptation plans in the face of tropical cyclones, allowing them to assess the effects of climate change on tropical cyclone incidents from an academic humanitarian logistics viewpoint in the forthcoming years.

Environmental Factors linked to the Presence of Aedes aegypti Larvae and the Prevalence of Dengue Hemorrhagic Fever

This study aims to examine the effect of climate and the presence of Aedes aegypti larvae on the prevalence of Dengue Hemorrhagic Fever (DHF) in Bukittinggi. In particular, the study was conducted in order to reduce the prevalence of DHF through vector control (Aedes aegypti) guided by the mosquito larvae free rate by proposing a model for environmental management in an Aedes aegypti larva-free area in Bukittinggi. Rainfall, air temperature, and humidity in 2015-2019 in Bukittinggi were measured to analyze their effect on the prevalence of dengue fever. Samples of data on the prevalence of dengue cases were carried out in total population against data on the prevalence of dengue cases, which amounted to 686 cases, and data on mosquito larvae free rates during 2015-2019. By using Pearson correlation analysis, the results show that the average air temperature in Bukittinggi over the last 5 years allows mosquitoes to survive because they have an average air temperature that functions as an optimum breeding vector. High rainfall can be expected to increase the breeding places of the Aedes aegypti so that the population will increase also has an impact on increasing cases in that month and several months later. Furthermore, the results confirm that there is no significant relationship and also no correlation between physical environmental factors, such as air temperature, humidity, and rainfall with the prevalence of dengue cases in Bukittinggi during the 2015-2019 period. Based on the pattern of distribution of DHF cases in Bukittinggi during the 2015-2019 period, controlling the prevalence of DHF cases needs to focus on activities in areas/villages that are endemic for DHF, without neglecting areas/villages where the prevalence of DHF cases is low, both at the temperature of the air and the mosquitoes will cause dengue fever experience optimal development, low, medium, and high rainfall, as well as in humidity where mosquitoes will experience ideal development.

Extreme Precipitation Events and Infectious Disease Risk: A Scoping Review and Framework for Infectious Respiratory Viruses

Extreme precipitation events (EPE) change the natural and built environments and alter human behavior in ways that facilitate infectious disease transmission. EPEs are expected with high confidence to increase in frequency and are thus of great public health importance. This scoping review seeks to summarize the mechanisms and severity of impacts of EPEs on infectious diseases, to provide a conceptual framework for the influence of EPEs on infectious respiratory diseases, and to define areas of future study currently lacking in this field. The effects of EPEs are well-studied with respect to enteric, vector-borne, and allergic illness where they are shown to moderately increase risk of illness, but not well-understood in relation to infectious respiratory illness. We propose a framework for a similar influence of EPEs on infectious respiratory viruses through several plausible pathways: decreased UV radiation, increased ambient relative humidity, and changes to human behavior (increased time indoors and use of heating and cooling systems). However, limited work has evaluated meteorologic risk factors for infectious respiratory diseases. Future research is needed to evaluate the effects of EPEs on infectious respiratory diseases using individual-level case surveillance, fine spatial scales, and lag periods suited to the incubation periods of the disease under study, as well as a full characterization of susceptible, vulnerable, and sensitive population characteristics.

The Complex Epidemiological Relationship between Flooding Events and Human Outbreaks of Mosquito-Borne Diseases: A Scoping Review

BACKGROUND

Climate change is expected to increase the frequency of flooding events. Although rainfall is highly correlated with mosquito-borne diseases (MBD) in humans, less research focuses on understanding the impact of flooding events on disease incidence. This lack of research presents a significant gap in climate change-driven disease forecasting.

OBJECTIVES

We conducted a scoping review to assess the strength of evidence regarding the potential relationship between flooding and MBD and to determine knowledge gaps.

METHODS

PubMed, Embase, and Web of Science were searched through 31 December 2020 and supplemented with review of citations in relevant publications. Studies on rainfall were included only if the operationalization allowed for distinction of unusually heavy rainfall events. Data were abstracted by disease (dengue, malaria, or other) and stratified by post-event timing of disease assessment. Studies that conducted statistical testing were summarized in detail.

RESULTS

From 3,008 initial results, we included 131 relevant studies (dengue n = 45 , malaria n = 61 , other MBD n = 49 ). Dengue studies indicated short-term (< 1  month ) decreases and subsequent (1-4 month) increases in incidence. Malaria studies indicated post-event incidence increases, but the results were mixed, and the temporal pattern was less clear. Statistical evidence was limited for other MBD, though findings suggest that human outbreaks of Murray Valley encephalitis, Ross River virus, Barmah Forest virus, Rift Valley fever, and Japanese encephalitis may follow flooding.

DISCUSSION

Flooding is generally associated with increased incidence of MBD, potentially following a brief decrease in incidence for some diseases. Methodological inconsistencies significantly limit direct comparison and generalizability of study results. Regions with established MBD and weather surveillance should be leveraged to conduct multisite research to a) standardize the quantification of relevant flooding, b) study nonlinear relationships between rainfall and disease, c) report outcomes at multiple lag periods, and d) investigate interacting factors that modify the likelihood and severity of outbreaks across different settings. https://doi.org/10.1289/EHP8887.

Relationship between the Incidence of Dengue Virus Transmission in Traditional Market and Climatic Conditions in Kaohsiung City

In 2014 and 2015, Southern Taiwan experienced two unprecedented outbreaks, with more than 10,000 laboratory-confirmed dengue cases in each outbreak. The present study was aimed to investigate the influence of meteorological and spatial factors on dengue outbreaks in Southern Taiwan and was conducted in Kaohsiung City, which is the most affected area in Taiwan. The distributed lag nonlinear model was used to investigate the role of climatic factors in the 2014 and 2015 dengue outbreaks. Spatial statistics in the Geographic Information System was applied to study the relationship between the dengue spreading pattern and locations of traditional markets (human motility) in the 2015 dengue outbreak. Meteorological analysis results suggested that the relative risk of dengue fever increased when the weekly average temperature was more than 15°C at lagged weeks 5 to 18. Elevated relative risk of dengue was observed when the weekly average rainfall was more than 150 mm at lagged weeks 12 to 20. The spatial analysis revealed that approximately 83% of dengue cases were located in the 1000 m buffer zone of traditional market, with statistical significance. These findings support the influence of climatic factors and human motility on dengue outbreaks. Furthermore, the study analysis may help authorities to identify hotspots and decide the timing for implementation of dengue control programs.

Impact of heavy precipitation events on pathogen occurrence in estuarine areas of the Puzi River in Taiwan

Pathogen populations in estuarine areas are dynamic, as they are subject to multiple natural and anthropogenic challenges. Heavy rainfall events bring instability to the aquatic environment in estuaries, causing changes in pathogen populations and increased environmental sanitation and public health concerns. In this study, we investigated the effects of heavy precipitation on the occurrence of pathogens in the Puzi River estuary, which is adjacent to the largest inshore oyster farming area in Taiwan. Our results indicated that Vibrio parahaemolyticus and adenovirus were the most frequently detected pathogens in the area. There was a significant difference (Mann-Whitney U test, p < 0.01) in water quality parameters, including total coliform, Escherichia coli, water temperature, turbidity, salinity, and dissolved oxygen, between groups with and without V. parahaemolyticus. In addition, the detection rate was negatively correlated with the average daily rainfall (r2 > 0.8). There was no significant difference between water quality parameters and the presence/absence of adenovirus, but a positive correlation was observed between the average daily rainfall and the detection rate of adenovirus (r2 ≥ 0.75). We conclude that heavy precipitation changes estuarine water quality, causing variations in microbial composition, including pathogens. As extreme weather events become more frequent due to climate change, the potential impacts of severe weather events on estuarine environments require further investigation.

Spatial Variability of Water Resources State of Regions around the “Belt and Road”

Water resource has become a key constraint for implementing the “Belt and Road” initiative which was raised by the Chinese government. Besides the study of spatial and temporal variability of precipitation, this study created a water hazard risk map along the “Belt and Road” zone through combined flood and drought data from 1985. Our results showed that South-Eastern Asia, southern China and eastern Southern Asia are areas with the most abundant precipitations, while floods in these areas are also the most serious. Northwest China, Western Asia, Northern Africa and Southern Asia are areas highly vulnerable to drought. Furthermore, the potential influence of flood and drought were also analyzed by associating with population distribution and corridor map. It reveals that China, South-Eastern Asia, Southern Asia, Western Asia and Northern Africa have the largest population number facing potential high water hazard risk. China–India–Burma Corridor and China–Indo-China Peninsula Corridor have the largest areas facing potential high water hazard risk.

Impact of Climate Change on Eye Diseases and Associated Economical Costs

Climate change generates negative impacts on human health. However, little is known about specific impacts on eye diseases, especially in arid and semi-arid areas where increases in air temperatures are expected. Therefore, the main goals of this research are: (i) to highlight the association between common eye diseases and environmental factors; and (ii) to analyze, through the available literature, the health expenditure involved in combating these diseases and the savings from mitigating the environmental factors that aggravate them. Mixed methods were used to assess the cross-variables (environmental factors, eye diseases, health costs). Considering Southern Spain as an example, our results showed that areas with similar climatic conditions could increase eye diseases due to a sustained increase in temperatures and torrential rains, among other factors. We highlight that an increase in eye diseases in Southern Spain is conditioned by the effects of climate change by up to 36.5%; the economic burden of the main eye diseases, extrapolated to the rest of the country, would represent an annual burden of 0.7% of Spain’s Gross Domestic Product. In conclusion, the increase in eye diseases has a strong economic and social impact that could be reduced with proper management of the effects of climate change. We propose a new concept: disease sink, defined as any climate change mitigation action which reduces the incidence or morbidity of disease.

Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan

Background

Dengue is the most rapidly spreading vector-borne disease globally, with a 30-fold increase in global incidence over the last 50 years. In Bhutan, dengue incidence has been on the rise since 2004, with numerous outbreaks reported across the country. The aim of this study was to identify and map areas that are vulnerable to dengue in Bhutan.

Methodology/Principal findings

We conducted a multicriteria decision analysis (MCDA) using a weighted linear combination (WLC) to obtain a vulnerability map of dengue. Risk factors (criteria) were identified and assigned with membership values for vulnerability according to the available literature. Sensitivity analysis and validation of the model was conducted to improve the robustness and predictive ability of the map. Our study revealed marked differences in geographical vulnerability to dengue by location and season. Low-lying areas and those located along the southern border were consistently found to be at higher risk of dengue. The vulnerability extended to higher elevation areas including some areas in the Capital city Thimphu during the summer season. The higher risk was mostly associated with relatively high population density, agricultural and built-up landscapes and relatively good road connectivity.

Conclusions

Using MCDA, our study identified vulnerable areas in Bhutan during specific seasons when and where the transmission of dengue is most likely to occur. This study provides evidence for the National Vector-borne Disease Control programme to optimize the use of limited public health resources for surveillance and vector control, to mitigate the public health threat of dengue.

Dengue is an important vector-borne viral disease affecting humans. In Bhutan, dengue incidence is on the rise with increased frequency of outbreaks and spread to new areas. Outbreaks were reported from places as high as above 900m above sea level in recent years. However, dengue control activities in Bhutan are usually initiated at the time of outbreaks. This often leads to a large number of cases and overburden the health system. To address these issues, we developed dengue risk maps at a fine spatial resolution by combining risk factors that mediate the transmission of dengue using a weighted linear combination. Vulnerability to dengue was spatially heterogeneous and varied by season. Dengue is highly vulnerable in low-lying areas throughout the season. However, the vulnerability extended to higher geographical elevations including the nation’s capital during the summer season. The study provides a firm evidence-base to prioritize areas and seasons for dengue control strategies in Bhutan.

UAV-based evaluation of morphological changes induced by extreme rainfall events in meandering rivers

Morphological changes, caused by the erosion and deposition processes due to water discharge and sediment flux occur, in the banks along the river channels and in the estuaries. Flow rate is one of the most important factors that can change river morphology. The geometric shapes of the meanders and the river flow parameters are crucial components in the areas where erosion or deposition occurs in the meandering rivers. Extreme precipitation triggers erosion on the slopes, which causes significant morphological changes in large areas during and after the event. The flow and sediment amount observed in a river basin with extreme precipitation increases and exceeds the long-term average value. Hereby, erosion severity can be determined by performing spatial analyses on remotely sensed imagery acquired before and after an extreme precipitation event. Changes of erosion and deposition along the river channels and overspill channels can be examined by comparing multi-temporal Unmanned Aerial Vehicle (UAV) based Digital Surface Model (DSM) data. In this study, morphological changes in the Büyük Menderes River located in the western Turkey, were monitored with pre-flood (June 2018), during flood (January 2019), and post-flood (September 2019) UAV surveys, and the spatial and volumetric changes of eroded/deposited sediment were quantified. For this purpose, the DSAS (Digital Shoreline Analysis System) method and the DEM of Difference (DoD) method were used to determine the changes on the riverbank and to compare the periodic volumetric morphological changes. Hereby, Structure from Motion (SfM) photogrammetry technique was exploited to a low-cost UAV derived imagery to achieve riverbank, areal and volumetric changes following the extreme rainfall events extracted from the time series of Tropical Rainfall Measuring Mission (TRMM) satellite data. The change analyses were performed to figure out the periodic morphodynamic variations and the impact of the flood on the selected meandering structures. In conclusion, although the river water level increased by 0.4-5.9 meters with the flood occurred in January 2019, the sediment deposition areas reformed after the flood event, as the water level decreased. Two-year monitoring revealed that the sinuosity index (SI) values changed during the flood approached the pre-flood values over time. Moreover, it was observed that the amount of the deposited sediments in September 2019 approached that of June 2018.

Early ground axe technology in Wallacea: The first excavations on Obi Island

The first excavations on Obi Island, north-east Wallacea, reveal three phases of occupation beginning in the terminal Pleistocene. Ground shell artefacts appear at the end of the terminal Pleistocene, the earliest examples in Wallacea. In the subsequent early Holocene occupation phase, ground stone axe flakes appear, which are again the earliest examples in Wallacea. Ground axes were likely instrumental to subsistence in Obi’s dense tropical forest. From ~8000 BP there was a hiatus lasting several millennia, perhaps because increased precipitation and forest density made the sites inhospitable. The site was reoccupied in the Metal Age, with this third phase including quadrangular ground stone artefacts, as well as pottery and pigs; reflecting Austronesian influences. Greater connectivity at this time is also indicated by an Oliva shell bead tradition that occurs in southern Wallacea and an exotic obsidian artefact. The emergence of ground axes on Obi is an independent example of a broader pattern of intensification at the Pleistocene-Holocene transition in Wallacea and New Guinea, evincing human innovation in response to rapid environmental change.

Transmission dynamics of dengue and chikungunya in a changing climate: do we understand the eco-evolutionary response?

INTRODUCTION

We are witnessing an alarming increase in the burden and range of mosquito-borne arboviral diseases. The transmission dynamics of arboviral diseases is highly sensitive to climate and weather and is further affected by non-climatic factors such as human mobility, urbanization, and disease control. As evidence also suggests, climate-driven changes in species interactions may trigger evolutionary responses in both vectors and pathogens with important consequences for disease transmission patterns.

AREAS COVERED

Focusing on dengue and chikungunya, we review the current knowledge and challenges in our understanding of disease risk in a rapidly changing climate. We identify the most critical research gaps that limit the predictive skill of arbovirus risk models and the development of early warning systems, and conclude by highlighting the potentially important research directions to stimulate progress in this field.

EXPERT OPINION

Future studies that aim to predict the risk of arboviral diseases need to consider the interactions between climate modes at different timescales, the effects of the many non-climatic drivers, as well as the potential for climate-driven adaptation and evolution in vectors and pathogens. An important outcome of such studies would be an enhanced ability to promulgate early warning information, initiate adequate response, and enhance preparedness capacity.

A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate

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A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate.

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Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO₂ emission and population density) are used in the model.

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The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China.

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A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase.

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The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results.

COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions.

In this paper, infection rate of COVID-19 is modelled globally at a 0.5^∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO² and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries’ reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment.

The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.

Geoprocessing and spatial analysis for identifying leptospirosis risk areas: a systematic review

Leptospirosis is a reemerging zoonosis caused by bacteria of the genus Leptospira sp. with global importance in the medical and veterinary fields, being responsible for about 59 thousand deaths each year in the world. The use of Geographic Information Systems (GIS) in the health sector is propitious and has been adopted by human and animal health professionals as an important tool in spatial analyses of health. The objective of this study was to conduct a systematic review on the geoprocessing and spatial analysis techniques adopted for mapping risk areas of human and animal leptospirosis. The articles were collected on scientific platforms by entering the following terms: SIG/GIS, leptospirose/leptospirosis, area de risco/risk area and distribuicao espacial/spatial distribution, and included in the study if they met the following criteria: a) publication in the period from 1998 to 2017; b) identification of risk areas and/or spatial distribution of leptospirosis as one of the research topics; and c) application of GIS in the methodology. As a result, we found 40 articles, published by 15 different countries, which adopted GIS for the spatial analysis and identification of risk areas of leptospirosis. Among these, only 45% (18) conducted an spatial statistical analysis. Brazil and USA had the highest numbers of publications, 16 and 7 articles, respectively. From 2007, the use of GIS and spatial analysis techniques, applied to the theme of this study, have been intensified and diversified, and 93% of the articles elected for this review were published from 2007 to 2017. The results point to a progressive interest of health professionals in applying these techniques for monitoring and conducting epidemiological analyses of leptospirosis, besides indicating a greater need for intersectoral integration between health professionals and others, in the use of spatial analysis and GIS techniques.

Estimating the Threshold Effects of Climate on Dengue: A Case Study of Taiwan

Climate change is regarded as one of the major factors enhancing the transmission intensity of dengue fever. In this study, we estimated the threshold effects of temperature on Aedes mosquito larval index as an early warning tool for dengue prevention. We also investigated the relationship between dengue vector index and dengue epidemics in Taiwan using weekly panel data for 17 counties from January 2012 to May 2019. To achieve our goals, we first applied the panel threshold regression technique to test for threshold effects and determine critical temperature values. Data were then further decomposed into different sets corresponding to different temperature regimes. Finally, negative binomial regression models were applied to assess the non-linear relationship between meteorological factors and Breteau index (BI). At the national level, we found that a 1°C temperature increase caused the expected value of BI to increase by 0.09 units when the temperature is less than 27.21 °C, and by 0.26 units when the temperature is greater than 27.21 °C. At the regional level, the dengue vector index was more sensitive to temperature changes because double threshold effects were found in the southern Taiwan model. For southern Taiwan, as the temperature increased by 1°C, the expected value of BI increased by 0.29, 0.63, and 1.49 units when the average temperature was less than 27.27 °C, between 27.27 and 30.17 °C, and higher than 30.17 °C, respectively. In addition, the effects of precipitation and relative humidity on BI became stronger when the average temperature exceeded the thresholds. Regarding the impacts of climate change on BI, our results showed that the potential effects on BI range from 3.5 to 54.42% under alternative temperature scenarios. By combining threshold regression techniques with count data regression models, this study provides evidence of threshold effects between climate factors and the dengue vector index. The proposed threshold of temperature could be incorporated into the implementation of public health measures and risk prediction to prevent and control dengue fever in the future.

Epidemiologic features of shigellosis and associated climatic factors in Taiwan

The consistent, sporadic transmission of shigellosis in Taiwan necessitates an exploration of risk factors for the occurrence of shigellosis. The purpose of this study was to study the epidemiologic characteristics and the relationship between climatic factors and the incidence of shigellosis in Taiwan. We collected data from cases of shigellosis reported to the Taiwan Centers for Disease Control (Taiwan CDC) from 2001 to 2016. Climatic data were obtained from the Taiwan Central Weather Bureau. The relationships between weather variability and the incidence of shigellosis in Taiwan were determined via Poisson regression analyses. During the 16-year study period, a total of 4171 clinical cases of shigellosis were reported to the Taiwan CDC. Among them, 1926 (46.2%) were classified as confirmed cases. The incidence of shigellosis showed significant seasonality, with the majority of cases occurring in summertime (for oscillation, P < .001). The number of shigellosis cases started to increase when temperatures reached 21°C (r = 0.88, P < .001). Similarly, the number of shigellosis cases began to increase at a relative humidity of 70-74% (r = 0.75, P < .005). The number of shigellosis cases was positively associated with the mean temperature and relative humidity in the period preceding the infection. In conclusion, the occurrence of shigellosis is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures.

MVSE: An R-package that estimates a climate-driven mosquito-borne viral suitability index

Viruses, such as dengue, Zika, yellow fever and chikungunya, depend on mosquitoes for transmission. Their epidemics typically present periodic patterns, linked to the underlying mosquito population dynamics, which are known to be driven by natural climate fluctuations. Understanding how climate dictates the timing and potential of viral transmission is essential for preparedness of public health systems and design of control strategies. While various alternative approaches have been proposed to estimate local transmission potential of such viruses, few open-source, ready to use and freely available software tools exist.We developed the Mosquito-borne Viral Suitability Estimator (MVSE) software package for the R programming environment. MVSE estimates the index P, a novel suitability index based on a climate-driven mathematical expression for the basic reproductive number of mosquito-borne viruses. By accounting for local humidity and temperature, as well as viral, vector and human priors, the index P can be estimated for specific host and viral species in different regions of the globe.We describe the background theory, empirical support and biological interpretation of the index P. Using real-world examples spanning multiple epidemiological contexts, we further demonstrate MVSE's basic functionality, research and educational potentials.

Risk factors for human leptospirosis following flooding: A meta-analysis of observational studies

Leptospirosis is probably the most widespread zoonotic disease in the world especially in tropical countries. There has been an increase in individual studies, which assessed the frequency of leptospirosis in flood conditions. Some studies showed contact with floods was significantly associated with the occurrence of leptospirosis while other studies reported differently. The objective of this meta-analysis was to synthesize the evidence on the risk factors which are associated with human leptospirosis following flooding. We set up the inclusion criteria and searched for the original studies, addressing leptospirosis in human with related to flood in health-related electronic databases including PubMed, Embase, Ovid Medline, google scholar and Scopus sources. We used the terms ‘leptospirosis’, ‘flood’, ‘risk factor’ and terms from the categories were connected with “OR” within each category and by “AND” between categories. The initial search yielded 557 citations. After the title and abstract screening, 49 full-text papers were reviewed and a final of 18 observational studies met the pre-specified inclusion criteria. Overall, the pooled estimates of 14 studies showed that the contact with flooding was a significant factor for the occurrence of leptospirosis (pooled OR: 2.19, 95%CI: 1.48–3.24, I²:86%). On stratification, the strength of association was greater in the case-control studies (pooled OR: 4.01, 95%CI: 1.26–12.72, I²:82%) than other designs (pooled OR:1.77,95%CI:1.18–2.65, I²:87%). Three factors such as ‘being male’(pooled OR:2.06, 95%CI:1.29–2.83), the exposure to livestock animals (pooled OR: 1.95, 95%CI:1.26–2.64), the lacerated wound (pooled OR:4.35, 95%CI:3.07–5.64) were the risk factors significantly associated with the incidence of leptospirosis following flooding in the absence of within-study heterogeneity (I²: 0%). We acknowledge study limitations such as publication bias and type 2 statistical errors. We recommended flood control and other environmental modifications that are expected to reduce the risk of leptospiral infection, and a multi-sectoral effort to this aspect would have long-term benefits.

Risk factors associated with sustained circulation of six zoonotic arboviruses: a systematic review for selection of surveillance sites in non-endemic areas

Arboviruses represent a significant burden to public health and local economies due to their ability to cause unpredictable and widespread epidemics. To maximize early detection of arbovirus emergence in non-endemic areas, surveillance efforts should target areas where circulation is most likely. However, identifying such hotspots of potential emergence is a major challenge. The ecological conditions leading to arbovirus outbreaks are shaped by complex interactions between the virus, its vertebrate hosts, arthropod vector, and abiotic environment that are often poorly understood. Here, we systematically review the ecological risk factors associated with the circulation of six arboviruses that are of considerable concern to northwestern Europe. These include three mosquito-borne viruses (Japanese encephalitis virus, West Nile virus, Rift Valley fever virus) and three tick-borne viruses (Crimean-Congo hemorrhagic fever virus, tick-borne encephalitis virus, and louping-ill virus). We consider both intrinsic (e.g. vector and reservoir host competence) and extrinsic (e.g. temperature, precipitation, host densities, land use) risk factors, identify current knowledge gaps, and discuss future directions. Our systematic review provides baseline information for the identification of regions and habitats that have suitable ecological conditions for endemic circulation, and therefore may be used to target early warning surveillance programs aimed at detecting multi-virus and/or arbovirus emergence.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

Impacts of flood disasters in Nigeria: A critical evaluation of health implications and management

Floods lead to tremendous losses of property, infrastructure, business and increased risk of diseases. Floods are also the most frequent natural disasters, affecting over 2.8 billion people in the world and causing over 200 000 deaths over the past three decades. The World Health Organization categorised the 2012 flood disaster in Nigeria as the worst flood to have hit the country in the past 50 years. This study reviews flood disasters in Nigeria and how they have been managed over the past two decades. The extensive review of the literature is complemented by data obtained from Ajegunle, a community in Ajeromi-Ifelodun Local Government Area. Because of its proximity to water bodies, its large population and its small land mass, the Ajegunle community is highly susceptible to floods and outbreaks of waterborne diseases. The study also discusses the institutionalisation and development of disaster management in Nigeria. Further, it critically evaluates the disaster management framework and other current disaster management policies as well as the effectiveness and functions of the disaster management focus areas and government response. The study takes a historic approach to flood disasters, linking disaster management to human health with a special focus on flood-related infectious diseases, isolating waterborne diseases as being predominant. Quantitative and qualitative data were collected to develop an understanding of how the people of Ajegunle are affected by flood disasters. This study reveals poorly managed health reforms and argues that in spite of government's disaster management policies, there is an absence of organised and coordinated institutional structures to plan and respond to flood emergencies. It also revealed that diarrhoea outbreak was the predominant waterborne disease associated with flood disasters. Although Lagos State has been said to have the best flood preparedness plan in Nigeria, it has failed to reduce the yearly flood disasters and their impact on the health of the people. The article suggests a holistic approach by the government to get stakeholders, especially the health sector, more actively involved in disaster management planning.

KEYWORDS

disaster management; floods; waterborne diseases; Ajegunle; Lagos; Nigeria.

Waterborne Diseases Arising From Climate Change

As a result of increased frequency and intensity of heat waves, increased floods and droughts, change in climate will affect biological, physical, and chemical components of water through different paths thus enhancing the risk of waterborne diseases. Identifying the role of weather in waterborne infection is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. This chapter provides evidence that precipitation and temperature can affect directly or indirectly water quality and consequently affect the health human. This chapter also highlights the complex relationship between precipitation or temperature and transmission of waterborne disease such as diarrheal disease, gastroenteritis, cryptosporidiosis, giardiasis, and cholera.

Potential influence of temperature and precipitation on preterm birth rate in Puerto Rico

The preterm birth (PTB) rate for singletons born in the tropical Caribbean island Puerto Rico increased from 11.3% in 1994, which was comparable to rates in the U.S., to as high as 18.3% in 2006 before decreasing to 15.5% in 2012. A few studies have reported that weather extremes are associated with higher risk of preterm birth, however, the effects of ambient temperature and precipitation has not been well examined in Puerto Rico. We compiled child birth data from the National Center for Health Statistics and weather data from the National Oceanic and Atmospheric Administration from 1994 to 2012. We explored the association between the weather factors and PTB rates with a distributed lag non-linear model (DLNM). We did not find direct association of lagged effect of temperature on birth outcome over monthly timescales. Both high intensity and frequency of precipitation and high frequency of storm and flood events are associated with increased risk of PTB rates. While the weather factors do not explain the marked increase and decrease in PTB rate, we emphasize the negative effects on PTB from weather extremes particularly precipitation in Puerto Rico.

Mathematics of dengue transmission dynamics: Roles of vector vertical transmission and temperature fluctuations

A new deterministic model is designed and used to gain insight into the effect of seasonal variations in temperature and vector vertical transmission on the transmission dynamics of dengue disease. The model, which incorporates (among many other features) the dynamics of the immature dengue-competent mosquitoes, vertical transmission in the vector population, density-dependent larval mortality and temperature effects, is rigorously analysed and simulated using data relevant to the disease dynamics in Chiang Mai province of Thailand. The non-trivial disease-free equilibrium of the model is shown to be globally-asymptotically stable when the associated basic reproduction number of the model is less than unity. Numerical simulations of the model, using data relevant to the disease dynamics in the Chiang Mai province of Thailand, show that vertical transmission in the vector population has only marginal impact on the disease dynamics, and that the effect of vertical transmission is temperature-dependent (in particular, the effect of vertical transmission on the disease dynamics increases for values of the mean monthly temperature in the range[ 16 - 28 ] ∘ C, and decreases with increasing mean monthly temperature thereafter). It is further shown that dengue burden (as measured in terms of disease incidence) is maximized when the mean monthly temperature is in the range[ 26 - 28 ] ∘ C (and dengue burden decreases for mean monthly temperature values above28 ∘ C). Thus, this study suggests that anti-dengue control efforts should be intensified during the period when this temperature range is recorded in the Chiang Mai province (this occurs between June and August).

The Epidemiological Influence of Climatic Factors on Shigellosis Incidence Rates in Korea

Research has shown the effects of climatic factors on shigellosis; however, no previous study has evaluated climatic effects in regions with a winter seasonality of shigellosis incidence. We examined the effects of temperature and precipitation on shigellosis incidence in Korea from 2002–2010. The incidence of shigellosis was calculated based on data from the Korean Center for Disease Control and Prevention (KCDC, Cheongju, Korea), and a generalized additive model (GAM) was used to analyze the associations between the incidence and climatic factors. The annual incidence rate of shigellosis was 7.9 cases/million persons from 2002–2010. During 2007–2010, high incidence rates and winter seasonality were observed among those aged ≥65 years, but not among lower age groups. Based on the GAM model, the incidence of shigellosis is expected to increase by 13.6% and 2.9% with a temperature increase of 1 °C and a lag of two weeks and with a mean precipitation increase of 1 mm and a lag of five weeks after adjustment for seasonality, respectively. This study suggests that the incidence of shigellosis will increase with global climate change despite the winter seasonality of shigellosis in Korea. Public health action is needed to prevent the increase of shigellosis incidence associated with climate variations.

Measuring Mosquito-borne Viral Suitability in Myanmar and Implications for Local Zika Virus Transmission

INTRODUCTION

In South East Asia, mosquito-borne viruses (MBVs) have long been a cause of high disease burden and significant economic costs. While in some SEA countries the epidemiology of MBVs is spatio-temporally well characterised and understood, in others such as Myanmar our understanding is largely incomplete.

MATERIALS AND METHODS

Here, we use a simple mathematical approach to estimate a climate-driven suitability index aiming to better characterise the intrinsic, spatio-temporal potential of MBVs in Myanmar.

RESULTS

Results show that the timing and amplitude of the natural oscillations of our suitability index are highly informative for the temporal patterns of DENV case counts at the country level, and a mosquito-abundance measure at a city level. When projected at fine spatial scales, the suitability index suggests that the time period of highest MBV transmission potential is between June and October independently of geographical location. Higher potential is nonetheless found along the middle axis of the country and in particular in the southern corridor of international borders with Thailand.

DISCUSSION

This research complements and expands our current understanding of MBV transmission potential in Myanmar, by identifying key spatial heterogeneities and temporal windows of importance for surveillance and control. We discuss our findings in the context of Zika virus given its recent worldwide emergence, public health impact, and current lack of information on its epidemiology and transmission potential in Myanmar. The proposed suitability index here demonstrated is applicable to other regions of the world for which surveillance data is missing, either due to lack of resources or absence of an MBV of interest.

Determining environmental and anthropogenic factors which explain the global distribution of Aedes aegypti and Ae. albopictus

Background

Responsible for considerable global human morbidity and mortality, Aedes aegypti and Ae. albopictus are the primary vectors of several important human diseases, including dengue and yellow fever. Although numerous variables that affect mosquito survival and reproduction have been recorded at the local and regional scales, many remain untested at the global level, potentially confounding mapping efforts to date.

Methods

We develop a modelling ensemble of boosted regression trees and maximum entropy models using sets of variables previously untested at the global level to examine their performance in predicting the global distribution of these two vectors. The results show that accessibility, absolute humidity and annual minimum temperature are consistently the strongest predictors of mosquito presence. Both vectors are similar in their response to accessibility and humidity, but exhibit individual profiles for temperature. Their mapped ranges are therefore similar except at peripheral latitudes, where the range of Ae. albopictus extends further, a finding consistent with ongoing trapping studies. We show that variables previously identified as being relevant, including maximum and mean temperatures, enhanced vegetation index, relative humidity and population density, are comparatively weak performers.

Results

The variables identified represent three key biological mechanisms. Cold tolerance is a critical biological parameter, controlling both species' distribution northwards, and to a lesser degree for Ae. albopictus which has consequent greater inland suitability in North America, Europe and East Asia. Absolute humidity restricts the distribution of both vectors from drier areas, where moisture availability is very low, and increases their suitability in coastal areas. The latter is exacerbated by accessibility with increased likelihood of vector importation due to greater potential for human and trade movement.

Conclusion

Accessibility, absolute humidity and annual minimum temperatures were the strongest and most robust global predictors of Ae. aegypti and Ae. albopictus presence, which should be considered in control efforts and future distribution projections.

The geo-spatial distribution of childhood diarrheal disease in West Africa, 2008-2013: A covariate-adjusted cluster analysis

Diarrhea is a major cause of morbidity and mortality among children in West Africa. To determine whether there are areas of heightened risk and if so, how they may be influenced by household and climatic variables, we describe the geo-spatial distribution of childhood diarrhea in ten countries of West Africa for the period 2008-2013 using data from Demographic and Health Surveys. The purely spatial scan statistic was applied, where the observed diarrhea cases were modeled as a Poisson variable and were compared to expected cases predicted from non-spatial logistic regression. Covariate-adjusted cluster analysis detected statistically significant clusters (p < 0.05) in ten cities and thirteen largely rural areas. Areas with particularly high relative risk included Cotonou, Benin (7.16), and Kaduna, Nigeria (7.21). The study demonstrates the importance of development and adaptation measures to protect child health, and that these interventions should be tailored to meet the needs of specific populations.

Meteorological factors affecting dengue incidence in Davao, Philippines

Background

Dengue fever is a major public health concern in the Philippines, and has been a significant cause of hospitalizations and deaths among young children. Previous literature links climate change to dengue, and with increasingly unpredictable changing climate patterns, there is a need to understand how these meteorological variables affect dengue incidence in a highly endemic area.

Methods

Weekly dengue incidences (2011–2015) in Davao Region, Philippines were obtained from the Department of Health. Same period of weekly local meteorological variables were obtained from the National Climatic Data Center (NCDC) and the National Oceanic and Atmospheric Administration (NOAA). Wavelet coherence analysis was used to determine the presence of non-stationary relationships, while a quasi-Poisson regression combined with distributed lag nonlinear model (DLNM) was used to analyze the association between meteorological variables and dengue incidences.

Results

Significant periodicity was detected in the 7 to 14-week band between the year 2011–2012 and a 26-week periodicity from the year 2013–2014. Overall cumulative risks were particularly high for rainfall at 32 mm (RR: 1.67, 95% CI: 1.07–2.62), while risks were observed to increase with increasing dew point. On the other hand, lower average temperature of 26 °C has resulted to an increased RR of dengue (RR: 1.96, 95% CI: 0.47–8.15) while higher temperature from 27 °C to 31 °C has lower RR.

Conclusions

The observed possible threshold levels of these meteorological variables can be integrated into an early warning system to enhance dengue prediction for better vector control and management in the future.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5532-4) contains supplementary material, which is available to authorized users.

Incidences of Waterborne and Foodborne Diseases After Meteorologic Disasters in South Korea

BACKGROUND

Climate change could increase the number of regions affected by meteorologic disasters. Meteorologic disasters can increase the risk of infectious disease outbreaks, including waterborne and foodborne diseases. Although many outbreaks of waterborne diseases after single disasters have been analyzed, there have not been sufficient studies reporting comprehensive analyses of cases occurring during long-term surveillance after multiple disasters, which could provide evidence of whether meteorologic disasters cause infectious disease outbreaks.

OBJECTIVES

This study aimed to assess the nationwide short-term changes in waterborne and foodborne disease incidences after a meteorologic disaster.

METHODS

We analyzed cases after all 65 floods and typhoons between 2001 and 2009 using the Korean National Emergency Management Agency's reports. Based on these data, we compared the weekly incidences of Vibrio vulnificus septicemia (VVS), shigellosis, typhoid fever, and paratyphoid fever before, during, and after the disasters, using multivariate Poisson regression models. We also analyzed the interactions between disaster characteristics and the relative risk of each disease.

FINDINGS

Compared with predisaster incidences, the incidences of VVS and shigellosis were 2.49-fold (95% confidence interval, 1.47-4.22) and 3.10-fold (95% confidence interval, 1.21-7.92) higher, respectively, the second week after the disaster. The incidences of VVS and shigellosis peaked the second week postdisaster and subsequently decreased. The risks of typhoid and paratyphoid fever did not significantly increase throughout the 4 weeks postdisaster. The daily average precipitation interacted with VVS and shigellosis incidences, whereas disaster type only interacted with VVS incidence patterns.

CONCLUSIONS

The incidences of VVS and shigellosis were associated with meteorologic disasters, and disaster characteristics were associated with the disease incidence patterns postdisaster. These findings provide important comprehensive evidence to develop and support policies for managing and protecting public health after meteorologic disasters.

How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China-A Bayesian Spatial Analysis

Evidence indicated that socio-environmental factors were associated with occurrence of Japanese encephalitis (JE). This study explored the association of climate and socioeconomic factors with JE (2006–2014) in Shaanxi, China. JE data at the county level in Shaanxi were supplied by Shaanxi Center for Disease Control and Prevention. Population and socioeconomic data were obtained from the China Population Census in 2010 and statistical yearbooks. Meteorological data were acquired from the China Meteorological Administration. A Bayesian conditional autoregressive model was used to examine the association of meteorological and socioeconomic factors with JE. A total of 1197 JE cases were included in this study. Urbanization rate was inversely associated with JE incidence during the whole study period. Meteorological variables were significantly associated with JE incidence between 2012 and 2014. The excessive precipitation at lag of 1–2 months in the north of Shaanxi in June 2013 had an impact on the increase of local JE incidence. The spatial residual variations indicated that the whole study area had more stable risk (0.80–1.19 across all the counties) between 2012 and 2014 than earlier years. Public health interventions need to be implemented to reduce JE incidence, especially in rural areas and after extreme weather.

Visceral leishmaniasis cyclical trends in Bihar, India - implications for the elimination programme

Background: Visceral leishmaniasis (VL) is a vector-borne disease of public health importance in India, with the highest burden of disease in the states of Bihar, Jharkhand, West Bengal and Uttar Pradesh. The disease is currently targeted for elimination (annual incidence to less than one per 10,000 population) using indoor residual spraying, active case detection and treatment. Historically the disease trend in India has been regarded as cyclical with case resurgence characteristically occurring every 15 years.  Understanding this pattern is essential if the VL elimination gains are to be sustained. To better understand the cyclical trends, annual climatic indicators including rainfall, temperature and humidity over time were compared with annual VL case incidence data. 

Methods: Annual climate data (rainfall, average and maximum temperature and specific humidity) from 1956-2004 were used to identify potential factors influencing VL incidence.  Months relevant to the VL life-cycle were identified and defined (Monsoon, Sand-fly Peak, Pre-Sand-fly Peak and Annual) for analysis. The Kruskall-Wallis test was used to determine significant difference between categorical rainfall and VL incidence, whilst univariate negative binomial regression models were used to determine predictors of disease incidence.

Results: The negative binomial regression model showed statistically significant associations (p <0.05) for VL incidence and maximum temperature, and average temperature, when considering annual and pre-sand fly peak time periods. No other associations between humidity, rainfall or temperature and VL incidence were detected (all values p >0.05). 

Conclusion: The VL programme in Bihar has made significant progress in adopting best practices for improved treatment and vector control, with the aim to achieve VL elimination.  However, open access granular programme data for indoor residual spray activities and case detection is required to fully understand the role of climate in disease transmission and potential resurgence.

The Re-Emergence and Emergence of Vector-Borne Rickettsioses in Taiwan

Rickettsial diseases, particularly vector-borne rickettsioses (VBR), have a long history in Taiwan, with studies on scrub typhus and murine typhus dating back over a century. The climatic and geographic diversity of Taiwan's main island and its offshore islands provide many ecological niches for the diversification and maintenance of rickettsiae alike. In recent decades, scrub typhus has re-emerged as the most prevalent type of rickettsiosis in Taiwan, particularly in eastern Taiwan and its offshore islands. While murine typhus has also re-emerged on Taiwan's western coast, it remains neglected. Perhaps more alarming than the re-emergence of these rickettsioses is the emergence of newly described VBR. The first case of human infection with Rickettsia felis was confirmed in 2005, and undetermined spotted fever group rickettsioses have recently been detected. Taiwan is at a unique advantage in terms of detecting and characterizing VBR, as it has universal health coverage and a national communicable disease surveillance system; however, these systems have not been fully utilized for this purpose. Here, we review the existing knowledge on the eco-epidemiology of VBR in Taiwan and recommend future courses of action.

Heavy precipitation as a risk factor for shigellosis among homeless persons during an outbreak - Oregon, 2015-2016

OBJECTIVES

Shigella species are the third most common cause of bacterial gastroenteritis in the United States. During a Shigella sonnei outbreak in Oregon from July 2015 through June 2016, Shigella cases spread among homeless persons with onset of the wettest rainy season on record.

METHODS

We conducted time series analyses using Poisson regression to determine if a temporal association between precipitation and shigellosis incidence existed. Models were stratified by housing status.

RESULTS

Among 105 infections identified, 45 (43%) occurred in homeless persons. With increasing precipitation, cases increased among homeless persons (relative risk [RR] = 1.36 per inch of precipitation during the exposure period; 95% confidence interval [CI] = 1.17-1.59), but not among housed persons (RR = 1.04; 95% CI 0.86-1.25).

CONCLUSIONS

Heavy precipitation likely contributed to shigellosis transmission among homeless persons during this outbreak. When heavy precipitation is forecast, organizations working with homeless persons could consider taking proactive measures to mitigate spread of enteric infections.

Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan

Background

Southern Taiwan has been a hotspot for dengue fever transmission since 1998. During 2014 and 2015, Taiwan experienced unprecedented dengue outbreaks and the causes are poorly understood. This study aims to investigate the influence of regional and local climate conditions on the incidence of dengue fever in Taiwan, as well as to develop a climate-based model for future forecasting.

Methodology/Principle findings

Historical time-series data on dengue outbreaks in southern Taiwan from 1998 to 2015 were investigated. Local climate variables were analyzed using a distributed lag non-linear model (DLNM), and the model of best fit was used to predict dengue incidence between 2013 and 2015. The cross-wavelet coherence approach was used to evaluate the regional El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables. The DLNM results highlighted the important non-linear and lag effects of minimum temperature and precipitation. Minimum temperature above 23°C or below 17°C can increase dengue incidence rate with lag effects of 10 to 15 weeks. Moderate to high precipitation can increase dengue incidence rates with a lag of 10 or 20 weeks. The model of best fit successfully predicted dengue transmission between 2013 and 2015. The prediction accuracy ranged from 0.7 to 0.9, depending on the number of weeks ahead of the prediction. ENSO and IOD were associated with nonstationary inter-annual patterns of dengue transmission. IOD had a greater impact on the seasonality of local climate conditions.

Conclusions/Significance

Our findings suggest that dengue transmission can be affected by regional and local climatic fluctuations in southern Taiwan. The climate-based model developed in this study can provide important information for dengue early warning systems in Taiwan. Local climate conditions might be influenced by ENSO and IOD, to result in unusual dengue outbreaks.

Climate change and human infectious diseases: A synthesis of research findings from global and spatio-temporal perspectives

The life cycles and transmission of most infectious agents are inextricably linked with climate. In spite of a growing level of interest and progress in determining climate change effects on infectious disease, the debate on the potential health outcomes remains polarizing, which is partly attributable to the varying effects of climate change, different types of pathogen-host systems, and spatio-temporal scales. We summarize the published evidence and show that over the past few decades, the reported negative or uncertain responses of infectious diseases to climate change has been growing. A feature of the research tendency is the focus on temperature and insect-borne diseases at the local and decadal scale. Geographically, regions experiencing higher temperature anomalies have been given more research attention; unfortunately, the Earth's most vulnerable regions to climate variability and extreme events have been less studied. From local to global scales, agreements on the response of infectious diseases to climate change tend to converge. So far, an abundance of findings have been based on statistical methods, with the number of mechanistic studies slowly growing. Research gaps and trends identified in this study should be addressed in the future.

Mobilisation of microbial indicators, microbial source tracking markers and pathogens after rainfall events

Climate change is expected to affect the Mediterranean region by causing an increase in the number of heavy rainfall events. The aim of this study was to assess the effect of extreme river flow variations due to rainfall on the persistence and mobilisation of various microorganisms. These included faecal pollution indicators (Escherichia coli (EC), somatic coliphages (SOMCPH) and sulphite reducing clostridia spores (SRC)), microbial source tracking indicators (Bacteroides thetaiotaomicron GA17 strain phages (GA17PH) and sorbitol fermenting bifidobacteria (SFBIF)), and two pathogens (Salmonella spp and Enterovirus). Water and sediment samples were taken at different distances from the river before and after heavy rainfall events. The microbial load was higher in sediment samples closer to the river course. The concentration of some faecal indicators (EC and SFBIF) increased in sediments and river water after rainfall events, whereas the most conservative parameter (SRC) showed almost no variation. After rainfall, the indicators persisted at a different rate. Salmonella spp and Enterovirus were detected in some samples but always at lower concentrations than the microbial indicators. In conclusion, sediments are reservoirs of faecal and MST indicators and pathogens and could therefore pose a risk of pathogen dissemination.

Association between meteorological factors and bacillary dysentery incidence in Chaoyang city, China: an ecological study

OBJECTIVES

To quantify the relationship between meteorological factors and bacillary dysentery incidence.

DESIGN

Ecological study.

SETTING

We collected bacillary dysentery incidences and meteorological data of Chaoyang city from the year 1981 to 2010. The climate in this city was a typical northern temperate continental monsoon. All meteorological factors in this study were divided into 4 latent factors: temperature, humidity, sunshine and airflow. Structural equation modelling was used to analyse the relationship between meteorological factors and the incidence of bacillary dysentery.

MATERIAL

Incidences of bacillary dysentery were obtained from the Center for Disease Control and Prevention of Chaoyang city, and meteorological data were collected from the Bureau of Meteorology in Chaoyang city.

PRIMARY OUTCOME MEASURES

The indexes including χ², root mean square error of approximation (RMSEA), comparative fit index (CFI), standardised root mean square residual (SRMR) and goodness-of-fit index (GFI) were used to evaluate the goodness-of-fit of the theoretical model to the data. The factor loads were used to explore quantitative relationship between bacillary dysentery incidences and meteorological factors.

RESULTS

The goodness-of-fit results of the model showing that RMSEA=0.08, GFI=0.84, CFI=0.88, SRMR=0.06 and the χ² value is 231.95 (p=0.0) with 15 degrees of freedom. Temperature and humidity factors had positive correlations with incidence of bacillary dysentery, with the factor load of 0.59 and 0.78, respectively. Sunshine had a negative correlation with bacillary dysentery incidence, with a factor load of -0.15.

CONCLUSIONS

Humidity and temperature should be given greater consideration in bacillary dysentery prevention measures for northern temperate continental monsoon climates, such as that of Chaoyang.

Empirical model for estimating dengue incidence using temperature, rainfall, and relative humidity: a 19-year retrospective analysis in East Delhi

OBJECTIVES

Aedes mosquitoes are responsible for transmitting the dengue virus. The mosquito lifecycle is known to be influenced by temperature, rainfall, and relative humidity. This retrospective study was planned to investigate whether climatic factors could be used to predict the occurrence of dengue in East Delhi.

METHODS

The number of monthly dengue cases reported over 19 years was obtained from the laboratory records of our institution. Monthly data of rainfall, temperature, and humidity collected from a local weather station were correlated with the number of monthly reported dengue cases. One-way analysis of variance was used to analyse whether the climatic parameters differed significantly among seasons. Four models were developed using negative binomial generalized linear model analysis. Monthly rainfall, temperature, humidity, were used as independent variables, and the number of dengue cases reported monthly was used as the dependent variable. The first model considered data from the same month, while the other three models involved incorporating data with a lag phase of 1, 2, and 3 months, respectively.

RESULTS

The greatest number of cases was reported during the post-monsoon period each year. Temperature, rainfall, and humidity varied significantly across the pre-monsoon, monsoon, and post-monsoon periods. The best correlation between these three climatic factors and dengue occurrence was at a time lag of 2 months.

CONCLUSIONS

This study found that temperature, rainfall, and relative humidity significantly affected dengue occurrence in East Delhi. This weather-based dengue empirical model can forecast potential outbreaks 2-month in advance, providing an early warning system for intensifying dengue control measures.

A Spatial Hierarchical Analysis of the Temporal Influences of the El Niño-Southern Oscillation and Weather on Dengue in Kalutara District, Sri Lanka

Dengue is the major public health burden in Sri Lanka. Kalutara is one of the highly affected districts. Understanding the drivers of dengue is vital in controlling and preventing the disease spread. This study focuses on quantifying the influence of weather variability on dengue incidence over 10 Medical Officer of Health (MOH) divisions of Kalutara district. Weekly weather variables and data on dengue notifications, measured at 10 MOH divisions in Kalutara from 2009 to 2013, were retrieved and analysed. Distributed lag non-linear model and hierarchical-analysis was used to estimate division specific and overall relationships between weather and dengue. We incorporated lag times up to 12 weeks and evaluated models based on the Akaike Information Criterion. Consistent exposure-response patterns between different geographical locations were observed for rainfall, showing increasing relative risk of dengue with increasing rainfall from 50 mm per week. The strongest association with dengue risk centred around 6 to 10 weeks following rainfalls of more than 300 mm per week. With increasing temperature, the overall relative risk of dengue increased steadily starting from a lag of 4 weeks. We found similarly a strong link between the Oceanic Niño Index to weather patterns in the district in Sri Lanka and to dengue at a longer latency time confirming these relationships. Part of the influences of rainfall and temperature can be seen as mediator in the causal pathway of the Ocean Niño Index, which may allow a longer lead time for early warning signals. Our findings describe a strong association between weather, El Niño-Southern Oscillation and dengue in Sri Lanka.