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1. A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera

   https://doi.org/10.3390/tropicalmed6030147  

   Usmani, Moiz; Brumfield, Kyle D.; Jamal, Yusuf; Huq, Anwar; Colwell, Rita R.; Jutla, Antarpreet (September 2021, Tropical Medicine and Infectious Disease)

   null (Ed.)

   Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal–oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction. 

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2. Vibrio cholerae: an opportunist of human crises. Journal of Medical Microbiology

   https://doi.org/JMM-D-20-00542  

   Grant, TA; Balasubramanian, D; Almagro-Moreno, S (January 2021, Journal of medical microbiology)

   Vibrio cholerae O1 is the etiological agent of the severe diarrheal disease cholera. Annually, there are an estimated 1-4 million cholera cases worldwide and over 140,000 deaths. The primary mode of disease transmission is through the consumption of water or food contaminated with the bacterium. Although cholera patients can be effectively treated using rehydration therapy, the disease remains a major scourge in areas with limited access to clean water and proper sanitation. Its continued prevalence highlights the failures of socioeconomic policies leading to wealth disparities, fragile and dated public infrastructure, and lack of appropriate health surveillance. 

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3. Online Learning with Optimism and Delay

   Flaspohler, Genevieve; Orabona, Francesco; Cohen, Judah; Mouatadid, Soukayna; Oprescu, Miruna; Orenstein, Paulo; Mackey, Lester (January 2021, Internation conference on machine learning)

   null (Ed.)

   Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms -- DORM, DORM+, and AdaHedgeD -- arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints can mitigate the regret penalty caused by delay. We pair this delay-as-optimism perspective with a new analysis of optimistic learning that exposes its robustness to hinting errors and a new meta-algorithm for learning effective hinting strategies in the presence of delay. We conclude by benchmarking our algorithms on four subseasonal climate forecasting tasks, demonstrating low regret relative to state-of-the-art forecasting models. 

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4. Online Learning with Optimism and Delay

   Flaspohler, Genevieve E; Orabona, Francesco; Cohen, Judah; Mouatadid, Soukayna; Oprescu, Miruna; Orenstein, Paulo; Mackey, Lester (July 2021, Proceedings of Machine Learning Research)

   Meila, Marina; Zhang, Tong (Ed.)

   Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms—DORM, DORM+, and AdaHedgeD—arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints can mitigate the regret penalty caused by delay. We pair this delay-as-optimism perspective with a new analysis of optimistic learning that exposes its robustness to hinting errors and a new meta-algorithm for learning effective hinting strategies in the presence of delay. We conclude by benchmarking our algorithms on four subseasonal climate forecasting tasks, demonstrating low regret relative to state-of-the-art forecasting models. 

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5. Impact of a multi-pronged cholera intervention in an endemic setting

   https://doi.org/10.1101/2023.12.14.23299970  

   Blake, Alexandre; Walder, Adam; Hanks, Ephraim; Welo, Placide Okitayembo; Luquero, Francisco; Bompangue, Didier; Bharti, Nita (December 2023, medRxiv)

   Abstract Cholera is a bacterial water-borne diarrheal disease transmitted via the fecal-oral route that causes high morbidity in sub-Saharan Africa and Asia. It is preventable with vaccination, and Water, Sanitation, and Hygiene (WASH) improvements. However, the impact of vaccination in endemic settings remains unclear. Cholera is endemic in the city of Kalemie, on the shore of Lake Tanganyika, in the Democratic Republic of Congo, where both seasonal mobility and the lake, a potential environmental reservoir, may promote transmission. Kalemie received a vaccination campaign and WASH improvements in 2013-2016. We assessed the impact of this intervention to inform future control strategies in endemic settings. We fit compartmental models considering seasonal mobility and environmentally-based transmission. We estimated the number of cases the intervention avoided, and the relative contributions of the elements promoting local cholera transmission. We estimated the intervention avoided 5,259 cases (95% credible interval: 1,576.6-11,337.8) over 118 weeks. Transmission did not rely on seasonal mobility and was primarily environmentally-driven. Removing environmental exposure or contamination could control local transmission. Repeated environmental exposure could maintain high population immunity and decrease the impact of vaccination in similar endemic areas. Addressing environmental exposure and contamination should be the primary target of interventions in such settings. Author summaryCholera is a major global health concern that causes high morbidity. It is a bacterial water-borne disease that can be transmitted via the fecal-oral route or the ingestion of contaminated water. Hence, both population mobility and environmental exposure can promote cholera persistence. The primary tools to prevent cholera include vaccination and Water, Sanitation, and Hygiene (WASH) improvements. The effectiveness of these interventions is well understood in epidemic settings, but their impact in endemic settings is unclear. Achieving cholera elimination requires disentangling the contributors to transmission, specifically population mobility and aquatic reservoirs, and assessing the impact of interventions performed in endemic settings.This study focuses on Kalemie, a cholera endemic city in the Democratic Republic of Congo, on shore of a lake that serves as a potential environmental reservoir. It quantifies the short-term impact of an intervention that used targeted vaccination and WASH. The study shows that the impact of vaccination was dampened by very high background immunity due to constant environmental exposure. This suggests that WASH improvements should be the primary intervention in such settings despite the time- and resource-intensive nature of implementation. 

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