Abstract The causative agent of cholera, Vibrio cholerae, is a bacterium native to the aquatic environment and commensal to zooplankton, namely copepods. V. cholerae thrives in warm, moderately saline water and its incidence is strongly influenced by environmental factors, which have proven critical for predictive awareness of cholera by identifying outbreak locations and timing. Susceptible‐Infected‐Recovered (SIR) models provide useful information for understanding transmission dynamics and epidemic curves of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability parameters derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) (R2 = 0.769) and Nigeria (R2 = 0.756), that incorporate data for temperature, precipitation, and drought index and have been calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models can be used for reasonably accurate retrospective analyses at both country‐wide scale for which they were calibrated and modified for smaller spatial extent, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic transmission will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools.

INTRODUCTION:Increasing aridity and incidence of droughts pose a significant threat to human health, primarily in exacerbating water scarcity, and is projected to become more frequent and severe as a result of related environmental changes in many regions globally. Concomitantly, water scarcity will force populations to utilize potentially contaminated water sources, hence increasing exposure to waterborne diseases, notably cholera. Proliferation of Vibrio cholerae, causative agent of cholera, is driven by environmental factors. Notably, temperature and precipitation have been employed in providing predictive awareness of cholera, allowing early warning and mitigation. The impact of droughts on incidence and spread of cholera is less understood. METHODS:This study aimed to quantify relationships among temperature, precipitation, and droughts as a basis for establishing the connection of environmental parameters and outbreaks of cholera. Thirteen cholera outbreaks between 2003 and 2023 in four African countries (Ethiopia, Kenya, Nigeria, and Senegal) were assessed using odds ratio and k-means clustering analysis. RESULTS:Cholera outbreaks were 3.07 (95 % CI: [0.95, 9.88]) times more likely when drought conditions (negative precipitation anomalies, positive temperature anomalies, and negative Standardized Precipitation-Evapotranspiration Index) were present, compared to their absence. When excess rainfall was also considered, the odds ratio increased to 3.50 (95 % CI: [1.03, 11.90]). Complementary evidence obtained using k-means clustering analysis supported the conclusion that outbreaks of cholera were common during drought conditions. CONCLUSIONS:Considering the last few decades with increased severity and frequency of droughts in cholera-impacted regions, climate projections indicate the threat of cholera outbreaks will continue, especially noting increasing reports of cholera globally. Hence, predictive intelligence systems for rapid risk assessment, with respect to climate, drought, and human health, are warranted.

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.

Cholera remains a global public health threat in regions where social vulnerabilities intersect with climate and weather processes that impact infectious Vibrio cholerae. While access to safe drinking water and sanitation facilities limit cholera outbreaks, sheer cost of building such infrastructure limits the ability to safeguard the population. Here, using Yemen as an example where cholera outbreak was reported in 2016, we show how predictive abilities for forecasting risk, employing sociodemographical, microbiological, and climate information of cholera, can aid in combating disease outbreak. An epidemiological analysis using Bradford Hill Criteria was employed in near-real-time to understand a predictive model's outputs and cholera cases in Yemen. We note that the model predicted cholera risk at least four weeks in advance for all governorates of Yemen with overall 72% accuracy (varies with the year). We argue the development of anticipatory decision-making frameworks for climate modulated diseases to design intervention activities and limit exposure of pathogens preemptively.

The occurrence of cases of COVID-19 suggests that it will likely become seasonally endemic in human populations. We seek to provide a quantification of the seasonality of the occurrence and severity of COVID-19 cases in human populations. Using global data, we show that the spatiotemporal distribution of COVID-19 cases is a function of distinct seasons and climates. We investigated this at the county and the country scale using a comparison of seasonal means, correlation analyses using ambient air temperatures and dew point temperatures, and multiple linear regression techniques. We found that most locations had the highest incidence of COVID-19 during winter compared to other seasons. Regions closer to the equator had a higher incidence of COVID-19 during the summer than regions further from the equator. Regions close to the equator, where mean annual temperatures have less variance compared to those further from the equator, had smaller differences between seasonal COVID-19 incidence. Correlation and regression analyses showed that ambient air and dew point temperatures were significantly associated with COVID-19 incidence. Our results suggest that temperature and the environment are influential factors to understand the transmission of COVID-19 within the human population. This research provides empirical evidence that temperature changes are a strong indicator of seasonal COVID-19 outbreaks, and as such it will aid in planning for future outbreaks and for mitigating their impacts.

Cholera, an ancient waterborne diarrheal disease, remains a threat to public health, especially when climate/weather processes, microbiological parameters, and sociological determinants intersect with population vulnerabilities of loss of access to safe drinking water and sanitation infrastructure. The ongoing war in Ukraine has either damaged or severely crippled civil infrastructure, following which the human population is at risk of health disasters. This editorial highlights a perspective on using predictive intelligence to combat potential (and perhaps impending) cholera outbreaks in various regions of Ukraine. Reliable and judicious use of existing earth observations inspired mathematical algorithms integrating heuristic understanding of microbiological, sociological, and weather parameters have the potential to save or reduce the disease burden.

The complexity of transmission of COVID-19 in the human population cannot be overstated. Although major transmission routes of COVID-19 remain as human-to-human interactions, understanding the possible role of climatic and weather processes in accelerating such interactions is still a challenge. The majority of studies on the transmission of this disease have suggested a positive association between a decrease in ambient air temperature and an increase in human cases. Using data from 19 early epicenters, we show that the relationship between the incidence of COVID-19 and temperature is a complex function of prevailing climatic conditions influencing human behavior that govern virus transmission dynamics. We note that under a dry (low-moisture) environment, notably at dew point temperatures below 0°C, the incidence of the disease was highest. Prevalence of the virus in the human population, when ambient air temperatures were higher than 24°C or lower than 17°C, was hypothesized to be a function of the interaction between humans and the built or ambient environment. An ambient air temperature range of 17 to 24°C was identified, within which virus transmission appears to decrease, leading to a reduction in COVID-19 human cases.