<i>Notes from the Field:</i> Ongoing Cholera Epidemic — Tanzania, 2015–2016

Narra, Rupa; Maeda, Justin; Temba, Herilinda; Mghamba, Janneth; Nyanga, Ali; Greiner, Ashley; Bakari, Muhammad; Beer, Karlyn D.; Chae, Sae-Rom; Curran, Kathryn; Eidex, Rachel B.; Gibson, James J.; Handzel, Thomas; Kiberiti, Stephen; Kishimba, Rogath; Lukupulo, Haji; Malibiche, Theophil; Massa, Khalid; Massay, Amani E; McCrickard, Lindsey S.; Mchau, Geofrey; Mmbaga, Vida; Mohamed, Ahmed Abade; Mwakapeje, Elibariki; Nestory, Emmanuel; Newton, Anna E.; Oyugi, Elvis; Rajasingham, Anu; Roland, Michelle E.; Rusibamayila, Neema; Sembuche, Senga; Urio, Loveness John; Walker, Tiffany; Wang, Alice; Quick, Robert

doi:10.15585/mmwr.mm6606a5

Cited by 13 publications

(15 citation statements)

References 3 publications

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“…This outbreak was characterized by large influxes of new arrivals, severe malnutrition, flooding, challenges to clean water access, and security concerns, all of which overwhelmed response efforts [50]. Another example occurred in camps in Tanzania in 2015, when a large countrywide cholera outbreak of nearly 22,000 cases overwhelmed general response efforts and caused 3,504 cases among refugees [51]. In this case, cholera likely did not originate in the refugee population but was associated with the general epidemic in the host country [52, 53].…”

Section: Discussionmentioning

confidence: 99%

Cholera prevention and control in refugee settings: Successes and continued challenges

Shannon¹,

Hast²,

Azman³

et al. 2019

PLoS Negl Trop Dis

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Section: Discussionmentioning

confidence: 99%

Cholera prevention and control in refugee settings: Successes and continued challenges

Shannon¹,

Hast²,

Azman³

et al. 2019

PLoS Negl Trop Dis

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“…Analogies for the potential impacts of climate change on water supply sources and water‐related diseases in Tanzania can be found in historical climatically extreme periods. For example, between August 2015 and early 2017, a large cholera outbreak caused over 25,000 infections across 23 of the 25 regions across the country; the reported Case Fatality Rate ranged from 1.1% to 1.5% (McCrickard et al, ; Narra et al, ). This large‐scale outbreak was related to a strong El Niño weather pattern that brought heavy rains to East Africa (Narra et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, between August 2015 and early 2017, a large cholera outbreak caused over 25,000 infections across 23 of the 25 regions across the country; the reported Case Fatality Rate ranged from 1.1% to 1.5% (McCrickard et al, ; Narra et al, ). This large‐scale outbreak was related to a strong El Niño weather pattern that brought heavy rains to East Africa (Narra et al, ). Climate change is expected to cause increases in temperature in Tanzania (Christensen et al, ) and further intensify existing extreme rainfall conditions (Shongwe et al, ; Vizy & Cook, ).…”

Section: Introductionmentioning

confidence: 99%

Understanding the Impacts of Short‐Term Climate Variability on Drinking Water Source Quality: Observations From Three Distinct Climatic Regions in Tanzania

et al. 2019

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Climate change is expected to increase waterborne diseases especially in developing countries.However, we lack understanding of how different types of water sources (both improved and unimproved) are affected by climate change, and thus, where to prioritize future investments and improvements to maximize health outcomes. This is due to limited knowledge of the relationships between source water quality and the observed variability in climate conditions. To address this gap, a 20-month observational study was conducted in Tanzania, aiming to understand how water quality changes at various types of sources due to short-term climate variability. Nine rounds of microbiological water quality sampling were conducted for Escherichia coli and total coliforms, at three study sites within different climatic regions. Each round included approximately 233 samples from water sources and 632 samples from households. To identify relationships between water quality and short-term climate variability, Bayesian hierarchical modeling was adopted, allowing these relationships to vary with source types and sampling regions to account for potentially different physical processes. Across water sources, increases in E. coli/total coliform levels were most closely related to increases in recent heavy rainfall. Our key recommendations to future longitudinal studies are (a) demonstrated value of high sampling frequency and temporal coverage (a minimum of 3 years) especially during wet seasons; (b) utility of the Bayesian hierarchical models to pool data from multiple sites while allowing for variations across space and water sources; and (c) importance of a multidisciplinary team approach with consistent commitment and sharing of knowledge.Plain Language Summary It is vital to understand how different types of water sources (both improved and unimproved) are influenced by changing climate conditions. This is needed to appreciate the reliability of these water sources in the future. A 20-month observational study was carried out in Tanzania to explore these relationships. Nine rounds of sampling were conducted across three study sites within different climatic regions, with fecal pathogen levels sampled at both water sources and households. A novel statistical model was developed to link water quality change with types of water quality and climate variability. We found that across different source types, the increases in fecal pathogen levels are most closely related to increases in recent heavy rainfall. We recommend that future studies to include at least 3 years of data collection. The successful study design here shows the value of multidisciplinary teams to ensure that appropriate statistical modeling structure can be used to analyze the data and provide new information for climate change adaptation. In the long term, such studies will provide evidence for decision-makers to prioritize future water investments and improvements to maximize public health outcomes. Key Points:• We present a longitudinal study in a developing country on wa...

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“…Over the past decades, there have been several studies and great achievements in developing epidemic models and systems for the proper prediction of cholera. However, the integration of weather variables and the use of machine learning techniques have not been deployed in modeling the cholera epidemics in Tanzania's settings [30]. This is due to the challenges that come with its datasets such as imbalanced data, missing information, and other uncertainties [31].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Imbalanced Cholera Dataset in Tanzania

Leo

Luhanga

Kisangiri

2019

The Scientific World Journal

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Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has strong linkage with environmental factors such as climate change and geographical location. Climate change has been strongly linked to the seasonal occurrence and widespread of cholera through the creation of weather patterns that favor the disease’s transmission, infection, and the growth of Vibrio cholerae, which cause the disease. Over the past decades, there have been great achievements in developing epidemic models for the proper prediction of cholera. However, the integration of weather variables and use of machine learning techniques have not been explicitly deployed in modeling cholera epidemics in Tanzania due to the challenges that come with its datasets such as imbalanced data and missing information. This paper explores the use of machine learning techniques to model cholera epidemics with linkage to seasonal weather changes while overcoming the data imbalance problem. Adaptive Synthetic Sampling Approach (ADASYN) and Principal Component Analysis (PCA) were used to the restore sampling balance and dimensional of the dataset. In addition, sensitivity, specificity, and balanced-accuracy metrics were used to evaluate the performance of the seven models. Based on the results of the Wilcoxon sign-rank test and features of the models, XGBoost classifier was selected to be the best model for the study. Overall results improved our understanding of the significant roles of machine learning strategies in health-care data. However, the study could not be treated as a time series problem due to the data collection bias. The study recommends a review of health-care systems in order to facilitate quality data collection and deployment of machine learning techniques.

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Notes from the Field: Ongoing Cholera Epidemic — Tanzania, 2015–2016

Cited by 13 publications

References 3 publications

Cholera prevention and control in refugee settings: Successes and continued challenges

Cholera prevention and control in refugee settings: Successes and continued challenges

Understanding the Impacts of Short‐Term Climate Variability on Drinking Water Source Quality: Observations From Three Distinct Climatic Regions in Tanzania

Machine Learning Model for Imbalanced Cholera Dataset in Tanzania

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