Trends and patterns of cholera epidemic in West Africa: a statistical modeling study

Author:

Conde Amadou12,Dureh Nurin1ORCID,Ueranantasun Attachai1

Affiliation:

1. a Department of Mathematics and Computer Science, Faculty of Sciences and Technology, Prince of Songkla University, Pattani, Thailand

2. b Julius Nyerere University of Kankan (UJNK), Kankan, Guinea

Abstract

Abstract Cholera is a serious disease that affects a huge number of people, especially in underdeveloped nations, and is particularly prevalent in Africa and southern Asia. This study aimed to determine cholera incidence trends and patterns in West Africa, as well as to develop a statistical model for cholera incidence. The outcomes of this study were occurrence, which was given a value of 1 if a case occurred and a value of 0 otherwise, and incidence rate. Logistic regression was used to model occurrence, while log-linear regression was used to model incidence after excluding the records with zero cases. The trend of cholera incidence rate was approximately constant for the Democratic Republic of Congo, whereas rates vary substantially throughout the study period in other countries. A confidence intervals plot shows that cholera incidence was higher in September and October, lower in 2015–2017, higher in Guinea, Niger, and Congo (west), and lower in Cote de-Ivoire, Cameroon, the Democratic Congo and Central African republics, Togo and Guinea Bissau. These two models can fit the data quite well. As a result, the method used in this study may be considered as an alternative to the traditional Poisson regression and negative binomial regression models.

Funder

Thailand’s Education Hub for ASEAN Countries (THE-AC), Prince of Songkla University

Publisher

IWA Publishing

Subject

Infectious Diseases,Microbiology (medical),Public Health, Environmental and Occupational Health,Waste Management and Disposal,Water Science and Technology

Reference20 articles.

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4. Exploring relationships between drought and epidemic cholera in Africa using generalised linear models;BMC Infectious Disease,2021

5. Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data;Journal of Water and Health,2020

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