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The Madras Presidency with its 26 districts: (1) Anantapur ; (2) Bellary; (3) Chingleput; (4) Chittoor; (5) Cuddapah; (6) Ganjam; (7) Godivari East; (8) Godivari West; (9) Guntur; (10) Kistna; (11) Kurnool; (12) neighborhood of Madras, the former capital city; (13) Malabar; (14) Nellore; (15) Nilgiris; (16) North Arcot; (17) Ramnad; (18) Salem; (19) South Arcot; (20) South Kanara; (21) Tanjore; (22) Tinnevelly; (23) Vizagapatam; (24) Trichinopoly; (25) Coimbatore; and (26) Madua.
Source publication
The seasonality of cholera and its spatial variability remain unexplained. Uncovering the role of environmental drivers in
these seasonal patterns is critical to understand temporal variability at longer time scales, including trends and interannual
variability. Rainfall has been proposed as a key driver of the seasonality of cholera. To address th...
Similar publications
We study changes in the bifurcations of seasonally driven compartmental epidemic models, where
the transmission rate is modulated temporally. In the presence of periodic modulation of the
transmission rate, the dynamics varies from periodic to chaotic. The route to chaos is typically
through period doubling bifurcation. There are coexisting attract...
Citations
... The temperature range of rise in cases was 15-40°C. The study by Ruiz-Moreno et al. [56] extensively investigated the rainfall-cholera relationship in Madras and explained the dual peak in annual cases by the differential effects of rainfall in endemic and epidemic areas. Generally, a complex relationship between rainfall and ambient temperature and cholera varies across regions (Table 8 and Table 9). ...
... During the monsoon, the dilutional effect of rainfall on water salinity leads to a reduction in the number of cases. After a lag period, due to increased contact with contaminated water, there is another peak of cases [56]. A study by Koelle et al. [71] in MATLAB, Bangladesh, demonstrated an association of outbreaks with monsoons and a lag period as long as eight months. ...
Studies on climate variables and food pathogens are either pathogen- or region-specific, necessitating a consolidated view on the subject. This study aims to systematically review all studies on the association of ambient temperature and precipitation on the incidence of gastroenteritis and bacteraemia from Salmonella, Shigella, Campylobacter, Vibrio, and Listeria species. PubMed, Ovid MEDLINE, Scopus, and Web of Science databases were searched up to 9 March 2023. We screened 3,204 articles for eligibility and included 83 studies in the review and three in the meta-analysis. Except for one study on Campylobacter, all showed a positive association between temperature and Salmonella, Shigella, Vibrio sp., and Campylobacter gastroenteritis. Similarly, most of the included studies showed that precipitation was positively associated with these conditions. These positive associations were found regardless of the effect measure chosen. The pooled incidence rate ratio (IRR) for the three studies that included bacteraemia from Campylobacter and Salmonella sp. was 1.05 (95 per cent confidence interval (95% CI): 1.03, 1.06) for extreme temperature and 1.09 (95% CI: 0.99, 1.19) for extreme precipitation. If current climate trends continue, our findings suggest these pathogens would increase patient morbidity, the need for hospitalization, and prolonged antibiotic courses.
... There was no shift in the seasonality of cholera after 1905, a pattern observed later when El Tor replaced the Classical strain [57] with a well-documented winter peak shift from December to mid-October, and also when O139 appeared in the 1990s. Differences in seasonality between strains, most likely reflect different ecological requirements between strains [60,61] that do not necessarily accompany antigenic and virulence changes. We do document a higher adult male mortality in the Punjab a few years after the 1904-07 event, a characteristic of the more recent replacements. ...
... Extremely heavy rains in the monsoon season may also have played an important role by disrupting sanitation systems and promoting with a delay the proliferation of bacteria in the environment due for example to increased nutrients. Evidence for a dual role of rainfall in the seasonal cycle of cholera with both a negative and a positive effect at different lags has been presented for historical cholera in endemic regions of Madras [60] and former Bengal [61]. Anomalous rainfall conditions can also affect harvest levels, enhancing population malnutrition and promoting famine and disease [62]. ...
Background
Explanations for the genesis and propagation of cholera pandemics since 1817 have remained elusive. Evolutionary pathogen change is presumed to have been a dominant factor behind the 7th “El Tor” pandemic, but little is known to support this hypothesis for preceding pandemics. The role of anomalous climate in facilitating strain replacements has never been assessed. The question is of relevance to guide the understanding of infectious disease emergence today and in the context of climate change.
Methodology/principal findings
We investigate the roles of climate and putative strain variation for the 6th cholera pandemic (1899–1923) using newly assembled historical records for climate variables and cholera deaths in provinces of former British India. We compare this historical pandemic with the 7th (El Tor) one and with the temporary emergence of the O139 strain in Bangladesh and globally. With statistical methods for nonlinear time series analysis, we examine the regional synchrony of outbreaks and associations of the disease with regional temperature and rainfall, and with the El Niño Southern Oscillation (ENSO). To establish future expectations and evaluate climate anomalies accompanying historical strain replacements, climate projections are generated with multi-model climate simulations for different 50-year periods.
The 6th cholera pandemic featured the striking synchronisation of cholera outbreaks over Bengal during the El Niño event of 1904–07, following the invasion of the Bombay Presidency with a delay of a few years. Accompanying anomalous weather conditions are similar to those related to ENSO during strain replacements and pandemic expansions into Africa and South America in the late 20th century. Rainfall anomalies of 1904–05 at the beginning of the large cholera anomaly fall in the 99th percentile of simulated changes for the regional climate.
Conclusions/significance
Evolutionary pathogen change can act synergistically with climatic conditions in the emergence and propagation of cholera strains. Increased climate variability and extremes under global warming provide windows of opportunity for emerging pathogens.
... While it is well-established that climate change is likely to exacerbate the occurrence of cholera in the region [1][2][3][4], it remains unclear how the relationship between cholera and climate has changed over time. Further, the majority of research has considered the question in relation to Bangladesh. ...
... Laboratory microcosm studies have demonstrated that the bacteria Vibrio Cholerae are better able to proliferate in warmer waters [39,40]. It is argued that this preference brings about an increased concentration of pathogenic Vibrio Cholerae bacteria in response to warmer temperatures [3]. An interesting comparison with our results can be found in analysis from Dhaka, an analogous city in Bangladesh, also within the Bengal Delta, 250 km North-East of Kolkata. ...
Background
In the Bengal Delta, research has shown that climate and cholera are linked. One demonstration of this is the relationship between interannual ocean-atmospheric oscillations such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). What remains unclear in the present literature is the nature of this relationship in the specific context of Kolkata, and how this relationship may have changed over time.
Results
In this study, we analyse the changing relationship between ENSO and IOD with cholera in Kolkata over recent (1999–2019) and historical (1897–1941) time intervals. Wavelet coherence analysis revealed significant non-stationary association at 2–4 year and 4–8 year periods between cholera and both interannual timeseries during both time intervals. However, coherence was notably weakened in the recent interval, particularly with regards to ENSO, a result supported by a complementary SARIMA analysis. Similar coherence patterns with temperature indicate it could be an important mediating factor in the relationship between cholera and oscillating climate phenomena in Kolkata.
Conclusions
This study reveals a shifting relationship between cholera and climate variables (ENSO and IOD) in Kolkata, suggesting a decoupling between environmental influences and cholera transmission in recent years. Our results therefore do not suggest that an intensification of ENSO is likely to significantly influence cholera in the region. We also find that the relationship between cholera and interannual climate variables is distinct to Kolkata, highlighting the spatial heterogeneity of the climate-cholera relationship even within the Bengal Delta.
... Within the Indian subcontinent, two distinct patterns emerge. The first is a single peak during the rainy season (May-August) with often zero reported cases outside of seasonal outbreaks [17,30]. This pattern is present in the drier parts of India including Delhi [54], Hyderabad [55], and Chandigarh [56]. ...
... This not only increases likelihood of contamination of the water source, but also increases the population at risk due to more people using the water source. This hypothesis may also explain the sudden drop in cases during the mid-monsoon lull in Bangladesh by suggesting that the sudden influx of water through surface runoff into the Ganges-Brahmaputra-Meghna (GBM) river system causes a dilution effect and 'washes out' the pathogenic bacteria [17,19]. ...
... Support for this hypothesis exists in water samples taken from Dhaka which demonstrate an inverse correlation between the presence of vibriophages capable of lysing a given serogroup of V. cholerae and the presence of a strain of that same serogroup [76]. However, Ruiz-Moreno et al. [17] pointed out that the phage hypothesis would imply a phage cycle with lags slightly behind the cholera cycle, which was not observed in the data. They also found that inclusion of this hypothesis into a mathematical model did not improve its ability to explain historic cholera mortality data in Bengal. ...
Objectives
Cholera has a long history in India and Bangladesh, the region where six out of the past seven global pandemics have been seeded. The changing climate and growing population have led to global cholera cases remaining high despite a consistent improvement in the access to clean water and sanitation. We aim to provide a holistic overview of variables influencing environmental cholera transmission within the context of India and Bangladesh, with a focus on the mechanisms by which they act.
Content
We identified 56 relevant texts (Bangladesh n = 40, India n = 7, Other n = 5). The results of the review found that cholera transmission is associated with several socio-economic and environmental factors, each associated variable is suggested to have at least one mediating mechanism. Increases in ambient temperature and coastal sea surface temperature support cholera transmission via increases in plankton and a preference of Vibrio cholerae for warmer waters. Increased rainfall can potentially support or reduce transmission via several mechanisms.
Summary and outlook
Common issues in the literature are co-variance of seasonal factors, limited access to high quality cholera data, high research bias towards research in Dhaka and Matlab (Bangladesh). A specific and detailed understanding of the relationship between SST and cholera incidence remains unclear.
... The importance of SVs in the incidence, prevalence, and outbreak of infectious diseases and their modelling has been gaining research interest from environmental, clinical, and global change perspectives. The significance of seasonality in the epidemiology of infectious diseases (especially those with high case-fatality rates such as cholera) has been documented in more than 34 countries in Sub-Saharan Africa and nations beyond Africa (Perez-Saez et al. 2022;De Magny et al., 2008;Ruiz-Moreno et al., 2007). In this study, the effects of SVs in environmental fluxes on PDP were modelled. ...
Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.
... Cholera, is an acute diarrheal disease, caused by the bacterium Vibrio cholerae that can result in severe morbidity and mortality; it has been associated with several climatic parameters, in situations with poor WASH and where cholera has already been seeded in the population [101][102][103]. For example, elevated ambient temperature is a key parameter for cholera incidence [104,105], as well as lower and higher precipitation [106,107]. The projected risk for non-cholera Vibrio cases, including gastroenteritis, wound infections, and septicemia, is projected to increase in the Baltic Sea region with higher sea surface temperatures [108]. ...
Climate change is adversely affecting the burden of infectious disease throughout the world, which is a health security threat. Climate-sensitive infectious disease includes vector-borne diseases such as malaria, whose transmission potential is expected to increase because of enhanced climatic suitability for the mosquito vector in Asia, sub-Saharan Africa, and South America. Climatic suitability for the mosquitoes that can carry dengue, Zika, and chikungunya is also likely to increase, facilitating further increases in the geographic range and longer transmission seasons, and raising concern for expansion of these diseases into temperate zones, particularly under higher greenhouse gas emission scenarios. Early spring temperatures in 2018 seem to have contributed to the early onset and extensive West Nile virus outbreak in Europe, a pathogen expected to expand further beyond its current distribution, due to a warming climate. As for tick-borne diseases, climate change is projected to continue to contribute to the spread of Lyme disease and tick-borne encephalitis, particularly in North America and Europe. Schistosomiasis is a water-borne disease and public health concern in Africa, Latin America, the Middle East, and Southeast Asia; climate change is anticipated to change its distribution, with both expansions and contractions expected. Other water-borne diseases that cause diarrheal diseases have declined significantly over the last decades owing to socioeconomic development and public health measures but changes in climate can reverse some of these positive developments. Weather and climate events, population movement, land use changes, urbanization, global trade, and other drivers can catalyze a succession of secondary events that can lead to a range of health impacts, including infectious disease outbreaks. These cascading risk pathways of causally connected events can result in large-scale outbreaks and affect society at large. We review climatic and other cascading drivers of infectious disease with projections under different climate change scenarios. Supplementary file1 (MP4 328467 KB).
... The seasonal patterns of cholera in coastal and estuarine areas in this region have been linked in part to the ecology of V cholerae in its natural brackish water habitats. 7,8 Case studies from individual countries over short time periods in SSA have shown diverse seasonal patterns in cholera occurrence, [9][10][11] although these fragmented descriptions have limited use in furthering our understanding of cholera dynamics and for global or regional public health planning. One of the major challenges hindering detailed large-scale descriptions of cholera seasonality has been the absence of unified, fine-scale spatial and temporal resolution datasets on cholera occurrence. ...
... The countries for which a model with no seasonality was best supported were Burundi, Central African Republic, Djibouti, Ghana, Liberia, Madagascar, Namibia, Senegal, Togo, and Zimbabwe. Among countries with seasonal cholera, 12 (50%) of 24 had evidence for within-country differences in seasonal cholera patterns (appendix pp [6][7][8][9]. ...
Background
Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality.
Methods
Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation).
Findings
24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding.
Interpretation
Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.
Funding
US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation.
... Seasonality is one important aspect of cholera epidemiology and cholera exhibits strong seasonal patterns in countries on the Bay of Bengal, the ancestral homeland of cholera. The seasonal patterns of cholera in coastal and estuarine areas in this region have been linked in part to the ecology of V. cholerae in its natural brackish water habitats 7,8 . Case studies from individual countries over short time-periods in SSA have demonstrated diverse seasonal patterns in cholera occurrence 9-11 though these fragmented descriptions have limited use in furthering our understanding of cholera dynamics and in global/regional public health planning. ...
Background
Cholera remains a major threat in Sub-Saharan Africa (SSA) where some of the highest case fatality risks are reported. Knowing in what months and where cholera tends to occur across the continent can aid in improving efforts to eliminate cholera as a public health concern; though largely due to lack of unified large-scale datasets, no continent-wide estimates exist. In this study we aim to estimate cholera seasonality across SSA.
Methods
We leverage the Global Task Force on Cholera Control (GTFCC) global cholera database with statistical models to synthesize data across spatial and temporal scale in order to infer the seasonality of excess suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and/or second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydro-climatic variables.
Findings
The majority of studied countries (24/34) have seasonal patterns in excess cholera, corresponding to approximately 85% of the SSA population. Most countries (19/24) also had sub-national differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macro-regions (West Africa and the Sahel vs. Eastern and Southern Africa), which were composed of sub-regional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation, and to a lesser extent with temperature and flooding.
Interpretation
Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.
Funding
The NASA Applied Sciences Program and the Bill and Melinda Gates Foundation.
... Traditional SEIR models are less successful in dealing with indirect modes of cholera transmission, most likely explaining why they are successful in predicting highly infectious human pathogen spread via direct human-tohuman contact (e.g., for viruses causing influenza and coronavirus disease 2019, COVID-19) compared to cholera, where indirect transmission plays a more important role [80]. The importance of indirect transmission routes has encouraged the incorporation of water quality models, seasonality, and climate-driven concepts into SEIR models [58,[97][98][99][100]. Seasonality is more often analyzed in regions prone to flooding after heavy precipitation, such as Bangladesh [57] and Yemen [101], where monsoons promote a bimodal peak of reported cholera cases [13,17]. ...
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.
... It was indicated that the transmission and incidence of cholera are significantly influenced by environmental parameters such as seasonality (Hashizume et al. 2010;Pascual et al. 2002), temperature (Luque Fernández et al. 2009;Olago et al. 2007), rainfall (Hashizume et al. 2008;Ruiz-Moreno et al. 2007b), sea surface temperatures (SST) (Bouma and Pascual 2001), and the El Niño Southern Oscillation (ENSO) events (Nkoko et al. 2011;Pascual et al. 2002;Ramírez 2014). Periodic variation in the sea surface temperature (El Niño) and the air pressure of the atmosphere (Southern Oscillation) in the equatorial Pacific Ocean is known as ENSO which has environmental and socio-economic effects globally (McPhaden et al. 2006;NOAA n.d.). ...
Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.
