4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      , ,
      Journal of Water and Health
      IWA Publishing

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references17

          • Record: found
          • Abstract: not found
          • Book: not found

          R: alanguage and environment for statistical computing

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Cholera in Cameroon, 2000-2012: Spatial and Temporal Analysis at the Operational (Health District) and Sub Climate Levels

            Introduction Recurrent cholera outbreaks have been reported in Cameroon since 1971. However, case fatality ratios remain high, and we do not have an optimal understanding of the epidemiology of the disease, due in part to the diversity of Cameroon’s climate subzones and a lack of comprehensive data at the health district level. Methods/Findings A unique health district level dataset of reported cholera case numbers and related deaths from 2000–2012, obtained from the Ministry of Public Health of Cameroon and World Health Organization (WHO) country office, served as the basis for the analysis. During this time period, 43,474 cholera cases were reported: 1748 were fatal (mean annual case fatality ratio of 7.9%), with an attack rate of 17.9 reported cases per 100,000 inhabitants per year. Outbreaks occurred in three waves during the 13-year time period, with the highest case fatality ratios at the beginning of each wave. Seasonal patterns of illness differed strikingly between climate subzones (Sudano-Sahelian, Tropical Humid, Guinea Equatorial, and Equatorial Monsoon). In the northern Sudano-Sahelian subzone, highest number of cases tended to occur during the rainy season (July-September). The southern Equatorial Monsoon subzone reported cases year-round, with the lowest numbers during peak rainfall (July-September). A spatial clustering analysis identified multiple clusters of high incidence health districts during 2010 and 2011, which were the 2 years with the highest annual attack rates. A spatiotemporal autoregressive Poisson regression model fit to the 2010–2011 data identified significant associations between the risk of transmission and several factors, including the presence of major waterbody or highway, as well as the average daily maximum temperature and the precipitation levels over the preceding two weeks. The direction and/or magnitude of these associations differed between climate subzones, which, in turn, differed from national estimates that ignored subzones differences in climate variables. Conclusions/Significance The epidemiology of cholera in Cameroon differs substantially between climate subzones. Development of an optimal comprehensive country-wide control strategy for cholera requires an understanding of the impact of the natural and built environment on transmission patterns at the local level, particularly in the setting of ongoing climate change.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Modeling cholera outbreaks.

              Mathematical modeling can be a valuable tool for studying infectious disease outbreak dynamics and simulating the effects of possible interventions. Here, we describe approaches to modeling cholera outbreaks and how models have been applied to explore intervention strategies, particularly in Haiti. Mathematical models can play an important role in formulating and evaluating complex cholera outbreak response options. Major challenges to cholera modeling are insufficient data for calibrating models and the need to tailor models for different outbreak scenarios.
                Bookmark

                Author and article information

                Contributors
                Journal
                Journal of Water and Health
                IWA Publishing
                1477-8920
                1996-7829
                February 01 2023
                January 23 2023
                February 01 2023
                January 23 2023
                : 21
                : 2
                : 261-270
                Article
                10.2166/wh.2023.241
                20bc9003-d58f-48f3-beb6-4a5a9d53ee37
                © 2023

                http://creativecommons.org/licenses/by-nc-nd/4.0/

                History

                Comments

                Comment on this article