Unexplored Opportunities: Use of Climate- and Weather-Driven Early Warning Systems to Reduce the Burden of Infectious Diseases

Advance Warning and Response Systems in Kenya: A Scoping Review

Introduction

Infectious diseases (IDs) cause approximately 13.7 million deaths globally. The Kenyan Advance Warning and Response Systems (AW&RS) against ID outbreaks is a core capacity of the 2005 International Health Regulations and a key indicator of health security. We mapped evidence on Kenya's AW&RS and their enablers, and barriers for successfully detecting IDs, including climate-sensitive IDs.

Methods

We searched Cochrane Library, MEDLINE, EMBASE, Web of Science, Africa Index Medicus, and SCOPUS before August 26th, 2024. We also searched for grey literature on the Google Scholar search engine alongside the main repositories of Kenyan Universities. Two independent reviewers conducted study selection, while one reviewer extracted data. Discrepancies were resolved through discussion. Results were synthesised narratively and thematically.

Results

The search yielded 4,379 records from databases and 1,363 articles from websites, university repositories, and citations; we included 166 articles in the analysis. Integrated Disease Surveillance and Response (IDSR) and cohort surveillance systems were the most common (37.2%). Most studies were concentrated in Nairobi County (25.7%) and reported on malaria (23.6%). Most systems (82.4%) monitored the disease burden and outbreaks using hospital-based data (35.1%) and automated alert mechanisms (27.7%). National bulletins report a temporal association between environmental factors and disease prevalence. Malaria, Rift Valley Fever (RVF), and cholera cases increased with higher precipitation, lower temperatures and increased vegetative index. AW&RS used the accuracy and reliability of the model prediction to measure the system's performance. Effectiveness was evaluated based on system acceptability and timeliness. Health system factors were predominant, with 121 enablers and 127 barriers. Key enablers included skilled personnel (13 studies), whereas inadequate finances were a major barrier (21 studies).

Conclusion

Most AW&RS were IDSR and cohort-based surveillance. Climate changes have resulted in observed trends in diseases such as malaria and RVF, but further studies are needed to determine causal links. Insufficient funding hinders the effective implementation of AW&RS. Future research should assess the cost drivers influencing system effectiveness.

Introducing new nurse leadership roles through an educational framework to protect the planet and human health

AIMS AND OBJECTIVES

This discussion paper proposes four new nursing leadership roles to address planetary health challenges.

BACKGROUND

Nurses are essential in reducing healthcare's greenhouse emissions. The Planetary Health Education Framework (PHEF) supports integrating planetary health concepts into sustainable healthcare practice. Nurse educators with planetary health expertise should teach the PHEF, while nurse climate advocates promote it to health professionals, with nurse-led planetary health researchers undertaking climate-change research and Planetary Health Nurse Practitioners practicing sustainable healthcare.

DESIGN

Discussion paper.

DATA SOURCES

A PICO framework was used to identify relevant articles: In nursing (P) should new roles be introduced (I) compared to no new roles (C) to understand climate change impacts on both nursing and planetary health (O).

DISCUSSION

Barriers to sustainable healthcare include denial, group-think, and ignorance. National and International organisation enablers highlight the importance of climate change in nurse education. The Australian College of Nursing recommends government funding for nurse-led planetary health research.

CONCLUSION

Educators should focus on curricula development, nurse climate advocates on education and policy, researchers on supporting planetary health research, and PHNPs on climate justice, environmental protection, and emissions reduction.

A framework for managing infectious diseases in rural areas in low- and middle-income countries in the face of climate change-East Africa as a case study

Climate change is having unprecedented impacts on human health, including increasing infectious disease risk. Despite this, health systems across the world are currently not prepared for novel disease scenarios anticipated with climate change. While the need for health systems to develop climate change adaptation strategies has been stressed in the past, there is no clear consensus on how this can be achieved, especially in rural areas in low- and middle-income countries that experience high disease burdens and climate change impacts simultaneously. Here, we highlight the need to put health systems in the context of climate change and demonstrate how this can be achieved by taking into account all aspects of infectious disease risk (i.e., pathogen hazards, and exposure and vulnerability to these pathogen hazards). The framework focuses on rural communities in East Africa since communities in this region experience climate change impacts, present specific vulnerabilities and exposure to climate-related hazards, and have regular exposure to a high burden of infectious diseases. Implementing the outlined approach can help make health systems climate adapted and avoid slowing momentum towards achieving global health grand challenge targets.

Radar versus optical: The impact of cloud cover when mapping seasonal surface water for health applications in monsoon-affected India

Surface water plays a vital role in the spread of infectious diseases. Information on the spatial and temporal dynamics of surface water availability is thus critical to understanding, monitoring and forecasting disease outbreaks. Before the launch of Sentinel-1 Synthetic Aperture Radar (SAR) missions, surface water availability has been captured at various spatial scales through approaches based on optical remote sensing data. A critical drawback of the latter is data loss due to cloud cover, however few studies have quantified this. This study evaluated data loss due to clouds in three Western Ghats (India) districts. These forest-agricultural mosaic landscapes, where water-related diseases are prevalent, experience the Indian monsoon. We compared surface water areas mapped by thresholding 10m Sentinel-1A SAR data with the optical 30m Landsat-derived Joint Research Centre (JRC) Global Surface Water product, currently the only globally available long-term monthly surface water data product. Backscatter thresholds were identified manually, and our Bayesian algorithm found these thresholds were very likely (>97%). While the Sentinel-1 SAR-based and JRC's optical-based approach mapped surface water extent with high overall accuracy (> 98%) when the cloud cover was low, the unmapped surface water area was substantial in the JRC product during the monsoon months. Across the districts, the average cloud cover in the July-August period was 92% or 90% for 2017 and 2018 respectively, resulting in 25% or 23% of the surface water area being unmapped. Also, the more detailed 10m resolution of Sentinel-1A SAR helped detect the many small water features missed by 30m JRC. Thus, for predicting water-related disease risks linked to small water features or monsoon rainfall, Sentinel-1A SAR is more effective. Finally, automatic backscatter thresholding for unvegetated surface water mapping can be effective if threshold values are adapted to regional-specific backscatter spatial and temporal variations.

Extreme weather events and disruptions to HIV services: a systematic review

BACKGROUND

Extreme weather events pose a risk to health and disproportionately affect vulnerable groups, such as people living with HIV. We aimed to investigate the effects of extreme weather events on HIV testing uptake, HIV treatment and care, and HIV transmission.

METHODS

For this systematic review, we searched PubMed, Web of Science, and PsycINFO for peer-reviewed studies published between database inception and Aug 31, 2023. Eligible studies were English-language qualitative, quantitative observational (retrospective, prospective, cross-sectional, longitudinal, case-control, and cohort), and mixed-method studies, and randomised controlled trials related to HIV and extreme weather events. We excluded reviews, mathematical models, and case reports. After exporting the search results, two authors independently screened the titles and abstracts of identified articles, reviewing the full text of those that met the inclusion criteria. We used systems thinking to develop a framework linking extreme weather events and HIV and summarised the results using thematic narrative synthesis.

FINDINGS

Of the 6126 studies identified by the search, 27 met the inclusion criteria and were eligible for analysis, of which 19 were quantitative, six were qualitative, and two were mixed-method studies. We identified five main themes linking extreme weather events to HIV: economic and livelihood conditions (12 studies), psychosocial factors (19 studies), infrastructure damage and operational challenges (17 studies), migration and displacement (ten studies), and associated medical conditions and health-care needs (12 studies). We showed how these themes interact in complex ways, resulting in a reduction in uptake of HIV testing, interruption of HIV care and subsequent disease progression, altered risk behaviours, and an increased prevalence of HIV.

INTERPRETATION

Extreme weather events are associated with disruptions to HIV services. Owing to the design of the included studies, we could not establish a causal relationship between extreme weather events and HIV incidence, highlighting a research gap. Appropriate adaptations and mitigation policies that protect the health and wellbeing of people living with HIV during and after extreme weather events are warranted. Such actions will be crucial to achieving the UNAIDS goal of ending HIV as a public health threat by 2030.

FUNDING

None.

The potential of remote sensing for improved infectious disease ecology research and practice

Outbreaks of COVID-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of infectious diseases on public health, ecosystems and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we (i) conduct a review of primary literature to quantify current uses of remote sensing in disease ecology; and (ii) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are enabling early detection of forest pathogens and that satellite data are most commonly used to study geographically widespread human diseases. Opportunities remain for increased use of data products that characterize vegetation, surface water and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal telemetry can support decision-making for disease management by providing insights into wildlife disease dynamics. Integrating these opportunities will advance both research and management of infectious diseases.

The impact of increasing temperatures due to climate change on infectious diseases

Global temperatures will continue to rise due to climate change, with high temperature periods expected to increase in intensity, frequency, and duration. Infectious diseases, including vector-borne diseases such as dengue fever and malaria, waterborne diseases such as cholera, and foodborne diseases such as salmonellosis are influenced by temperature and other climatic variables, thus contributing to higher disease burden and associated healthcare costs, particularly in socioeconomically disadvantaged regions. Targeted efforts and investments are therefore needed to support low and middle income countries to prepare for and respond to the increasing infectious disease threats posed by rising temperatures. This can be facilitated by the development and refinement of robust disease and entomological surveillance and early warning systems with integration of climatic information that promote enhanced understanding of the geographic distribution of disease risk. To enhance healthcare workforce capacity and capability to respond to these public health threats, medical curricula and continuing professional education programmes for healthcare providers must include evidence based components on the impacts of climate change on infectious diseases.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Climate change and infectious disease surveillance in Nepal: qualitative study exploring social, cultural, political and institutional factors influencing disease surveillance

BACKGROUND

To explore the impacts of contextual issues encompassing social, cultural, political and institutional elements, on the operation of public health surveillance systems in Nepal concerning the monitoring of infectious diseases in the face of a changing climate.

METHODS

Semi-structured interviews (n = 16) were conducted amongst key informants from the Department of Health Services, Health Information Management System, Department of Hydrology and Meteorology, World Health Organization, and experts working on infectious disease and climate change in Nepal, and data were analysed using thematic analysis technique.

RESULTS

Analysis explicates how climate change is constructed as a contingent risk for infectious diseases transmission and public health systems, and treated less seriously than other 'salient' public health risks, having implications for how resources are allocated. Further, analysis suggests a weak alliance among different stakeholders, particularly policy makers and evidence generators, resulting in the continuation of traditional practices of infectious diseases surveillance without consideration of the impacts of climate change.

CONCLUSIONS

We argue that along with strengthening systemic issues (epidemiological capacity, data quality and inter-sectoral collaboration), it is necessary to build a stronger political commitment to urgently address the influence of climate change as a present and exponential risk factor in the spread of infectious disease in Nepal.

The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide

To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Impacts of weather and air pollution on Legionnaires' disease in Switzerland: A national case-crossover study

BACKGROUND

The number of reported cases of Legionnaires' disease (LD) has risen markedly in Switzerland (6.5/100,000 inhabitants in 2021) and abroad over the last decade. Legionella, the causative agent of LD, are ubiquitous in the environment. Therefore, environmental changes can affect the incidence of LD, for example by increasing bacterial concentrations in the environment or by facilitating transmission.

OBJECTIVES

The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland.

METHODS

We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO2 levels available for the year 2019.

RESULTS

Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence.

DISCUSSION

Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.

An updated review on oral protein-based antigen vaccines efficiency and delivery approaches: a special attention to infectious diseases

Various infectious agents affect human health via the oral entrance. The majority of pathogens lack approved vaccines. Oral vaccination is a convenient, safe and cost-effective approach with the potential of provoking mucosal and systemic immunity and maintaining individual satisfaction. However, vaccines should overcome the intricate environment of the gastrointestinal tract (GIT). Oral protein-based antigen vaccines (OPAVs) are easier to administer than injectable vaccines and do not require trained healthcare professionals. Additionally, the risk of needle-related injuries, pain, and discomfort is eliminated. However, OPAVs stability at environmental and GIT conditions should be considered to enhance their stability and facilitate their transport and storage. These vaccines elicit the local immunity, protecting GIT, genital tract and respiratory epithelial surfaces, where numerous pathogens penetrate the body. OPAVs can also be manipulated (such as using specific incorporated ligand and receptors) to elicit targeted immune response. However, low bioavailability of OPAVs necessitates development of proper protein carriers and formulations to enhance their stability and efficacy. There are several strategies to improve their efficacy or protective effects, such as incorporation of adjuvants, enzyme inhibitors, mucoadhesive or penetrating devices and permeation enhancers. Hence, efficient delivery of OPAVs into GIT require proper delivery systems mainly including smart target systems, probiotics, muco-adhesive carriers, lipid- and plant-based delivery systems and nano- and microparticles.

Forecasting Outbreaks of Hantaviral Disease: Future Directions in Geospatial Modeling

Hantaviral diseases have been recognized as 'place diseases' from their earliest identification and, epidemiologically, are tied to single host species with transmission occurring from infectious hosts to humans. As such, human populations are most at risk when they are in physical proximity to suitable habitats for reservoir populations, when numbers of infectious hosts are greatest. Because of the lags between improving habitat conditions and increasing infectious host abundance and spillover to humans, it should be possible to anticipate (forecast) where and when outbreaks will most likely occur. Most mammalian hosts are associated with specific habitat requirements, so identifying these habitats and the ecological drivers that impact population growth and the dispersal of viral hosts should be markers of the increased risk for disease outbreaks. These regions could be targeted for public health and medical education. This paper outlines the rationale for forecasting zoonotic outbreaks, and the information that needs to be clarified at various levels of biological organization to make the forecasting of orthohantaviruses successful. Major challenges reflect the transdisciplinary nature of forecasting zoonoses, with needs to better understand the implications of the data collected, how collections are designed, and how chosen methods impact the interpretation of results.

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.

Communicating ocean and human health connections: An agenda for research and practice

The emergence of ocean and human health (OHH) science as a distinct scholarly discipline has led to increased research outputs from experts in both the natural and social sciences. Formal research on communication strategies, messaging, and campaigns related to OHH science remains limited despite its importance as part of the social processes that can make knowledge actionable. When utilized to communicate visible, local issues for targeting audiences, OHH themes hold the potential to motivate action in pursuit of solutions to environmental challenges, supplementing efforts to address large-scale, abstract, or politicized issues such as ocean acidification or climate change. Probing peer-reviewed literature from relevant areas of study, this review article outlines and reveals associations between society and the quality of coastal and marine ecosystems, as well as key themes, concepts, and findings in OHH science and environmental communication. Recommendations for future work concerning effective ocean and human health science communication are provided, creating a platform for innovative scholarship, evidence-based practice, and novel collaboration across disciplines.

Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers

Background

In Europe, the frequency, intensity, and geographic range of West Nile virus (WNV)-outbreaks have increased over the past decade, with a 7.2-fold increase in 2018 compared to 2017, and a markedly expanded geographic area compared to 2010. The reasons for this increase and range expansion remain largely unknown due to the complexity of the transmission pathways and underlying disease drivers. In a first, we use advanced artificial intelligence to disentangle the contribution of eco-climatic drivers to WNV-outbreaks across Europe using decade-long (2010-2019) data at high spatial resolution.

Methods

We use a high-performance machine learning classifier, XGBoost (eXtreme gradient boosting) combined with state-of-the-art XAI (eXplainable artificial intelligence) methodology to describe the predictive ability and contribution of different drivers of the emergence and transmission of WNV-outbreaks in Europe, respectively.

Findings

Our model, trained on 2010-2017 data achieved an AUC (area under the receiver operating characteristic curve) score of 0.97 and 0.93 when tested with 2018 and 2019 data, respectively, showing a high discriminatory power to classify a WNV-endemic area. Overall, positive summer/spring temperatures anomalies, lower water availability index (NDWI), and drier winter conditions were found to be the main determinants of WNV-outbreaks across Europe. The climate trends of the preceding year in combination with eco-climatic predictors of the first half of the year provided a robust predictive ability of the entire transmission season ahead of time. For the extraordinary 2018 outbreak year, relatively higher spring temperatures and the abundance of Culex mosquitoes were the strongest predictors, in addition to past climatic trends.

Interpretation

Our AI-based framework can be deployed to trigger rapid and timely alerts for active surveillance and vector control measures in order to intercept an imminent WNV-outbreak in Europe.

Funding

The work was partially funded by the Swedish Research Council FORMAS for the project ARBOPREVENT (grant agreement 2018-05973).

Environmental determinants predicting population vulnerability to high yellow fever incidence

Yellow fever (YF) is an endemic mosquito-borne disease in Brazil, though many locations have not observed cases in recent decades. Some locations with low disease burden may resemble locations with higher disease burden through environmental and ecohydrological characteristics, which are known to impact YF burden, motivating increased or continued prevention measures such as vaccination, mosquito control or surveillance. This study aimed to use environmental characteristics to estimate vulnerability to observing high YF burden among all Brazilian municipalities. Vulnerability was defined in three categories based on yearly incidence between 2000 and 2017: minimal, low and high vulnerability. A cumulative logit model was fit to these categories using environmental and ecohydrological predictors, selecting those that provided the most accurate model fit. Per cent of days with precipitation, mean temperature, biome, population density, elevation, vegetation and nearby disease occurrence were included in best-fitting models. Model results were applied to estimate vulnerability nationwide. Municipalities with highest probability of observing high vulnerability was found in the North and Central-West (2000-2016) as well as the Southeast (2017) regions. Results of this study serve to identify specific locations to prioritize new or ongoing surveillance and prevention of YF based on underlying ecohydrological conditions.

Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic reveals a major gap in global biosecurity infrastructure: a lack of publicly available biological samples representative across space, time, and taxonomic diversity. The shortfall, in this case for vertebrates, prevents accurate and rapid identification and monitoring of emerging pathogens and their reservoir host(s) and precludes extended investigation of ecological, evolutionary, and environmental associations that lead to human infection or spillover. Natural history museum biorepositories form the backbone of a critically needed, decentralized, global network for zoonotic pathogen surveillance, yet this infrastructure remains marginally developed, underutilized, underfunded, and disconnected from public health initiatives. Proactive detection and mitigation for emerging infectious diseases (EIDs) requires expanded biodiversity infrastructure and training (particularly in biodiverse and lower income countries) and new communication pipelines that connect biorepositories and biomedical communities. To this end, we highlight a novel adaptation of Project ECHO’s virtual community of practice model: Museums and Emerging Pathogens in the Americas (MEPA). MEPA is a virtual network aimed at fostering communication, coordination, and collaborative problem-solving among pathogen researchers, public health officials, and biorepositories in the Americas. MEPA now acts as a model of effective international, interdisciplinary collaboration that can and should be replicated in other biodiversity hotspots. We encourage deposition of wildlife specimens and associated data with public biorepositories, regardless of original collection purpose, and urge biorepositories to embrace new specimen sources, types, and uses to maximize strategic growth and utility for EID research. Taxonomically, geographically, and temporally deep biorepository archives serve as the foundation of a proactive and increasingly predictive approach to zoonotic spillover, risk assessment, and threat mitigation.

Health Risks Due To Climate Change: Inequity In Causes And Consequences

Climate change has altered global to local weather patterns and increased sea levels, and it will continue to do so. Average temperatures, precipitation amounts, and other variables such as humidity levels are all rising. In addition, weather variability is increasing, causing, for example, a greater number of heat waves, many of which are more intense and last longer, and more floods and droughts. These changes are collectively increasing the number of injuries, illnesses, and deaths from a wide range of climate-sensitive health outcomes. Future health risks will be determined not just by the hazards created by a changing climate but also by the sensitivity of individuals and communities exposed to these hazards and the capacity of health systems to prepare for and effectively manage the attendant risks. These risks include deaths and injuries from extreme events (for example, heat waves, storms, and floods), infectious diseases (including food-, water-, and vectorborne illnesses), and food and water insecurity. These risks are unevenly distributed and both create new inequities and exacerbate those that already exist. Most of these risks are projected to increase with each additional unit of warming. Using an equity lens to move beyond incremental to transformational resilience would reduce vulnerability and improve sustainability for all, but substantial additional funding is required for proactive and effective actions by the health system.

Climate and climate-sensitive diseases in semi-arid regions: a systematic review

OBJECTIVES

We aim to describe the relationships between climate variables and climate-sensitive diseases (CSDs) in semi-arid regions, highlighting the different main groups of CSDs and their climate patterns.

METHODS

This systematic review considered Medline, Science Direct, Scopus and Web of Science. The data collection period was August and September 2019 and included studies published between 2008 and 2019. This study followed a protocol based on the PRISMA statement. Data analysis was done in a qualitative way.

RESULTS

The most of works were from Africa, Asia and Iran (71%), where temperature was the main climatic variable. Although the studies provide climatic conditions that are more favorable for the incidence of vector-borne and respiratory diseases, the influence of seasonal patterns on the onset, development and end of CSDs is still poorly understood, especially for gastrointestinal disorders. Moreover, little is known about the impact of droughts on CSDs.

CONCLUSIONS

This review summarized the state of art of the relationship between climate and CSDs in semi-arid regions. Moreover, a research agenda was provided, which is fundamental for health policy development, priority setting and public health management.

Epidemiological models for predicting Ross River virus in Australia: A systematic review

Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used time-series models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation.

Effect of climatic factors on the seasonal fluctuation of human brucellosis in Yulin, northern China

Background

Brucellosis is a serious public health problem primarily affecting livestock workers. The strong seasonality of the disease indicates that climatic factors may play important roles in the transmission of the disease. However, the associations between climatic variability and human brucellosis are still poorly understood.

Methods

Data for a 14-year series of human brucellosis cases and seven climatic factors were collected in Yulin City from 2005 to 2018, one of the most endemic areas in northern China. Using cross-correlation analysis, the Granger causality test, and a distributed lag non-linear model (DLNM), we assessed the quantitative relationships and exposure-lag-response effects between monthly climatic factors and human brucellosis.

Results

A total of 7103 cases of human brucellosis were reported from 2005 to 2018 in Yulin City with a distinct peak between April and July each year. Seasonal fluctuations in the transmission of human brucellosis were significantly affected by temperature, sunshine duration, and evaporation. The effects of climatic factors were non-linear over the 6-month period, and higher values of these factors usually increased disease incidence. The maximum separate relative risk (RR) was 1.36 (95% confidence interval [CI], 1.03–1.81) at a temperature of 17.4 °C, 1.12 (95% CI, 1.03–1.22) with 311 h of sunshine, and 1.18 (95% CI, 0.94–1.48) with 314 mm of evaporation. In addition, the effects of these three climatic factors were cumulative, with the highest RRs of 2.27 (95% CI, 1.09–4.57), 1.54 (95% CI, 1.10–2.18), and 1.27 (95% CI, 0.73–2.14), respectively.

Conclusions

In Yulin, northern China, variations in climatic factors, especially temperature, sunshine duration, and evaporation, contributed significantly to seasonal fluctuations of human brucellosis within 6 months. The key determinants of brucellosis transmission and the identified complex associations are useful references for developing strategies to reduce the disease burden.

Climate change and infectious disease research in Nepal: Are the available prerequisites supportive enough to researchers?

Although Nepal has been identified as a country highly vulnerable to adverse health and socioeconomic impacts arising from climate change, extant research on climate sensitive infectious diseases has yet to develop the evidence base to adequately address these threats. In this opinion paper we identify and characterise basic requirements that are hindering the progress of climate change and infectious disease research in Nepal. Our opinion is that immediate attention should be given to strengthening Nepal's public health surveillance system, promoting inter-sectoral collaboration, improving public health capacity, and enhancing community engagement in disease surveillance. Moreover, we advocate for greater technical support of public health researchers, and data sharing among data custodians and epidemiologists/researchers, to generate salient evidence to guide relevant public health policy formulation aimed at addressing the impacts of climate change on human health in Nepal. International studies on climate variability and infectious diseases have clearly demonstrated that climate sensitive diseases, namely vector-borne and food/water-borne diseases, are sensitive to climate variation and climate change. This research has driven the development and implementation of climate-based early warning systems for preventing potential outbreaks of climate-sensitive infectious diseases across many European and African countries. Similarly, we postulate that Nepal would greatly benefit from a climate-based early warning system, which would assist in identification or prediction of conditions suitable for disease emergence and facilitate a timely response to reduce mortality and morbidity during epidemics.

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

BACKGROUND

Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far.

METHODS

Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach.

RESULTS

The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months.

CONCLUSIONS

This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4-month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.