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Geospatial Variability of Cholera Cases in Malawi Based on Climatic and Socioeconomic Influences

DOI: 10.4236/jgis.2024.161001, PP. 1-20

Keywords: Cholera, Geospatial Variability, Prevalence, GIS, MGWR, Vulnerability, Malawi

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Abstract:

Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.

References

[1]  Somboonwit, C., Menezes, L.J., Holt, D.A., Sinnott, J.T. and Shapshak, P. (2017) Current Views and Challenges on Clinical Cholera. Bioinformation, 13, 405-409.
https://doi.org/10.6026/97320630013405
[2]  Pezeshki, Z., Tafazzoli-Shadpour, M., Mansourian, A., Eshrati, B., Omidi, E. and Nejadqoli, I. (2012) Model of Cholera Dissemination Using Geographic Information Systems and Fuzzy Clustering Means: Case Study, Chabahar, Iran. Public Health, 126, 881-887.
https://doi.org/10.1016/j.puhe.2012.07.002
[3]  Fleming, G., Van Der Merwe, M. and McFerren, G. (2007) Fuzzy Expert Systems and GIS for Cholera Health Risk Prediction in Southern Africa. Environmental Modelling & Software, 22, 442-448.
https://doi.org/10.1016/j.envsoft.2005.12.008
[4]  Mukhopadhyay, A.K. (2015) Mapping of Cholera Cases Using Satellite Based Recording Systems to Investigate the Outbreak. Indian Journal of Medical Research, 142, 509-511.
https://doi.org/10.4103/0971-5916.171269
[5]  Lippi, D. and Gotuzzo, E. (2014) The Greatest Steps towards the Discovery of Vibrio Cholerae. Clinical Microbiology and Infection, 20, 191-195.
https://doi.org/10.1111/1469-0691.12390
[6]  Crooks, A.T. and Hailegiorgis, A.B. (2014) An Agent-Based Modeling Approach Applied to the Spread of Cholera. Environmental Modelling & Software, 62, 164-177.
https://doi.org/10.1016/j.envsoft.2014.08.027
[7]  Bishop, A.L. and Camilli, A. (2011) Vibrio Cholerae: Lessons for Mucosal Vaccine Design. Expert Review of Vaccines, 10, 79-94.
https://doi.org/10.1586/erv.10.150
[8]  Chao, D.L., Halloran, M.E. and Longini, I.M. (2011) Vaccination Strategies for Epidemic Cholera in Haiti with Implications for the Developing World. Proceedings of the National Academy of Sciences of the United States of America, 108, 7081-7085.
https://doi.org/10.1073/pnas.1102149108
[9]  Lee, E.C., et al. (2020) Achieving Coordinated National Immunity and Cholera Elimination in Haiti through Vaccination: A Modelling Study. The Lancet Global Health, 8, E1081-E1089.
https://doi.org/10.1016/S2214-109X(20)30310-7
[10]  Miggo, M., et al. (2023) Fight against Cholera Outbreak, Efforts and Challenges in Malawi. Health Science Reports, 6, e1594.
https://doi.org/10.1002/hsr2.1594
[11]  UNICEF and WHO Step up Efforts to Contain Cholera in Malawi and Call for Additional Funds and Support.
https://www.unicef.org/malawi/press-releases/unicef-and-who-step-efforts-contain-cholera-malawi-and-call-additional-funds-and
[12]  Bagcchi, S. (2022) Malawi Takes on Cholera Outbreak amid Cyclone Devastation. The Lancet Microbe, 3, e480.
https://doi.org/10.1016/S2666-5247(22)00131-8
[13]  Feinmann, J. (2023) The BMJ Appeal 2022-23: Cholera on the Rise and How IFRC Is Working to Fight It. BMJ, 380, O3007.
https://doi.org/10.1136/bmj.o3007
[14]  Msyamboza, K.P., Kagoli, M., M’bang’ombe, M., Chipeta, S. and Masuku, H.D. (2014) Cholera Outbreaks in Malawi in 1998-2012: Social and Cultural Challenges in Prevention and Control. The Journal of Infection in Developing Countries, 8, 720-726.
https://doi.org/10.3855/jidc.3506
[15]  Chinkaka, E., et al. (2023) Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021. Journal of Geographic Information System, 15, 110-121.
https://doi.org/10.4236/jgis.2023.151007
[16]  Bouma, M.J. and Pascual, M. (2001) Seasonal and Interannual Cycles of Endemic Cholera in Bengal 1891-1940 in Relation to Climate and Geography. Hydrobiologia, 460, 147-156.
https://doi.org/10.1023/A:1013165215074
[17]  Idoga, P.E., Toycan, M. and Zayyad, M.A. (2019) Analysis of Factors Contributing to the Spread of Cholera in Developing Countries. The Eurasian Journal of Medicine, 51, 121-127.
https://doi.org/10.5152/eurasianjmed.2019.18334
[18]  Leckebusch, G.C. and Abdussalam, A.F. (2015) Climate and Socioeconomic Influences on Interannual Variability of Cholera in Nigeria. Health Place, 34, 107-117.
https://doi.org/10.1016/j.healthplace.2015.04.006
[19]  Musa, G.J., et al. (2013) Use of GIS Mapping as a Public Health Tool—From Cholera to Cancer. Health Services Insights, 6, 111-116.
https://doi.org/10.4137/HSI.S10471
[20]  National Statistical Office (2019) Malawi 2018 Population and Housing Census Main Report. UNFPA Malawi.
https://malawi.unfpa.org/en/resources/malawi-2018-population-and-housing-census-main-report
[21]  Mulumpwa, M., Jere, W., Lazaro, M. and Mtethiwa, A. (2018) Modelling and Forecasting Lake Malawi Water Level Fluctuations Using Stochastic Models. African Journal of Rural Development, 3, 831-841.
[22]  PHIM.
https://phim.health.gov.mw/
[23]  Department of Climate Change and Meteorologial Services (DCCMS).
http://www.metmalawi.gov.mw/
[24]  Zheng, Q., et al. (2022) Cholera Outbreaks in Sub-Saharan Africa during 2010-2019: A Descriptive Analysis. International Journal of Infectious Diseases, 122, 215-221.
https://doi.org/10.1016/j.ijid.2022.05.039
[25]  W. H. O. R. O. for Africa (2023) Weekly Regional Cholera Bulletin: 3 July 2023.
https://iris.who.int/handle/10665/371043
[26]  Jutla, A., et al. (2013) Environmental Factors Influencing Epidemic Cholera. American Journal of Tropical Medicine and Hygiene, 89, 597-607.
https://doi.org/10.4269/ajtmh.12-0721
[27]  Constantin de Magny, G. and Colwell, R.R. (2009) Cholera and Climate: A Demonstrated Relationship. Transactions of the American Clinical and Climatological Association, 120, 119-128.
[28]  Luque Fernández, M.A., Bauernfeind, A., Jiménez, J.D., Gil, C.L., El Omeiri, N. and Guibert, D.H. (2009) Influence of Temperature and Rainfall on the Evolution of Cholera Epidemics in Lusaka, Zambia, 2003-2006: Analysis of a Time Series. Transactions of the Royal Society of Tropical Medicine and Hygiene, 103, 137-143.
https://doi.org/10.1016/j.trstmh.2008.07.017
[29]  Oloukoi, G., Bob, U. and Jaggernath, J. (2014) Perception and Trends of Associated Health Risks with Seasonal Climate Variation in Oke-Ogun Region, Nigeria. Health Place, 25, 47-55.
https://doi.org/10.1016/j.healthplace.2013.09.009
[30]  World Bank Climate Change Knowledge Portal.
https://climateknowledgeportal.worldbank.org/
[31]  Government of Malawi (2023) Malawi 2023 Tropical Cyclone Freddy Post-Disaster Needs Assessment. Government of Malawi, Lilongwe.
[32]  Pascual, M., Bouma, M.J. and Dobson, A.P. (2002) Cholera and Climate: Revisiting the Quantitative Evidence. Microbes and Infection, 4, 237-245.
https://doi.org/10.1016/S1286-4579(01)01533-7
[33]  Usmani, M., Brumfield, K.D., Jamal, Y., Huq, A., Colwell, R.R. and Jutla, A. (2021) A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera. Tropical Medicine and Infectious Disease, 6, 147.
https://doi.org/10.3390/tropicalmed6030147
[34]  Shackleton, D., Memon, F.A., Chen, A., Dutta, S., Kanungo, S. and Deb, A. (2023) The Changing Relationship between Cholera and Interannual Climate Variables in Kolkata over the Past Century. Gut Pathogens, 15, Article No. 42.
https://doi.org/10.1186/s13099-023-00565-w
[35]  Colwell, R.R. (1996) Global Climate and Infectious Disease: The Cholera Paradigm. Science, 274, 2025-2031.
https://doi.org/10.1126/science.274.5295.2025
[36]  Mavian, C., et al. (2020) Toxigenic Vibrio Cholerae Evolution and Establishment of Reservoirs in Aquatic Ecosystems. Proceedings of the National Academy of Sciences of the United States of America, 117, 7897-7904.
https://doi.org/10.1073/pnas.1918763117
[37]  Lobitz, B., et al. (2000) Climate and Infectious Disease: Use of Remote Sensing for Detection of Vibrio Cholerae by Indirect Measurement. Proceedings of the National Academy of Sciences of the United States of America, 97, 1438-1443.
https://doi.org/10.1073/pnas.97.4.1438
[38]  Emch, M., et al. (2008) Local Environmental Predictors of Cholera in Bangladesh and Vietnam. American Journal of Tropical Medicine and Hygiene, 78, 823-832.
https://doi.org/10.4269/ajtmh.2008.78.823
[39]  Davies-Teye, B.B.K., Vanotoo, L., Yabani, J.B. and Kwaakye-Maclean, C. (2015) Socio-Economic Factors Associated with Cholera Outbreak in Southern Ghana, 2012: A Case-Control Study. International Journal of Epidemiology, 44, I188.
https://doi.org/10.1093/ije/dyv096.289
[40]  Hsiao, A., Hall, A.H., Mogasale, V. and Quentin, W. (2018) The Health Economics of Cholera: A Systematic Review. Vaccine, 36, 4404-4424.
https://doi.org/10.1016/j.vaccine.2018.05.120
[41]  Richterman, A., Sainvilien, D.R., Eberly, L. and Ivers, L.C. (2018) Individual and Household Risk Factors for Symptomatic Cholera Infection: A Systematic Review and Meta-Analysis. The Journal of Infectious Diseases, 218, S154-S164.
https://doi.org/10.1093/infdis/jiy444
[42]  Kuna, A. and Gajewski, M. (2017) Cholera—The New Strike of an Old Foe. International Maritime Health, 68, 163-167.
https://doi.org/10.5603/IMH.2017.0029
[43]  Saha, A., et al. (2017) Socioeconomic Risk Factors for Cholera in Different Transmission Settings: An Analysis of the Data of a Cluster Randomized Trial in Bangladesh. Vaccine, 35, 5043-5049.
https://doi.org/10.1016/j.vaccine.2017.07.021
[44]  Andrade, C. (2021) Z Scores, Standard Scores, and Composite Test Scores Explained. Indian Journal of Psychological Medicine, 43, 555-557.
https://doi.org/10.1177/02537176211046525
[45]  Oshan, T.M., Li, Z., Kang, W., Wolf, L.J. and Stewart Fotheringham, A. (2019) MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS International Journal of Geo-Information, 8, Article No. 269.
https://doi.org/10.3390/ijgi8060269
[46]  Mansour, S., Alahmadi, M., Darby, S., Leyland, J. and Atkinson, P.M. (2023) Geospatial Modelling of Post-Cyclone Shaheen Recovery Using Nighttime Light Data and MGWR. International Journal of Disaster Risk Reduction, 93, Article ID: 103761.
https://doi.org/10.1016/j.ijdrr.2023.103761
[47]  Zumbo, B.D. (2016) Standard-Setting Methodology: Establishing Performance Standards and Setting Cut-Scores to Assist Score Interpretation. Applied Physiology, Nutrition, and Metabolism, 41, S74-S82.
https://doi.org/10.1139/apnm-2015-0522
[48]  Yu, H., Fotheringham, A., Li, Z., Oshan, T., Kang, W. and Wolf, L. (2019) Inference in Multiscale Geographically Weighted Regression. Geographical Analysis, 52, 87-106.
https://doi.org/10.31219/osf.io/4dksb
[49]  Wolf, L.J., Oshan, T.M. and Fotheringham, A.S. (2018) Single and Multiscale Models of Process Spatial Heterogeneity. Geographical Analysis, 50, 223-246.
https://doi.org/10.1111/gean.12147
[50]  Lu, B., Charlton, M., Harris, P. and Fotheringham, A.S. (2014) Geographically Weighted Regression with a Non-Euclidean Distance Metric: A Case Study Using Hedonic House Price Data. International Journal of Geographical Information Science, 28, 660-681.
https://doi.org/10.1080/13658816.2013.865739
[51]  Multiscale Geographically Weighted Regression (MGWR) (Spatial Statistics)—ArcGIS Pro|Documentation.
https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/multiscale-geographically-weighted-regression.htm
[52]  Deen, J., Mengel, M. and Clemens, J. (2019) Epidemiology of Cholera. Vaccine, 38, A31-A40.
https://doi.org/10.1016/j.vaccine.2019.07.078
[53]  Christaki, E., Dimitriou, P., Pantavou, K. and Nikolopoulos, G. (2020) The Impact of Climate Change on Cholera: A Review on the Global Status and Future Challenges. Atmosphere, 11, Article No. 449.
https://doi.org/10.3390/atmos11050449
[54]  Chowdhury, F.R., Nur, Z., Hassan, N., Von Seidlein, L. and Dunachie, S. (2017) Pandemics, Pathogenicity and Changing Molecular Epidemiology of Cholera in the Era of Global Warming. Annals of Clinical Microbiology and Antimicrobials, 16, Article No. 10.
https://doi.org/10.1186/s12941-017-0185-1
[55]  Koelle, K. and Pascual, M. (2004) Disentangling Extrinsic from Intrinsic Factors in Disease Dynamics: A Nonlinear Time Series Approach with an Application to Cholera. The American Naturalist, 163, 901-913.
https://doi.org/10.1086/420798
[56]  Kruger, S.E., Lorah, P.A. and Okamoto, K.W. (2022) Mapping Climate Change’s Impact on Cholera Infection Risk in Bangladesh. PLOS Global Public Health, 2, e0000711.
https://doi.org/10.1371/journal.pgph.0000711
[57]  Koelle, K., Rodó, X., Pascual, M., Yunus, M. and Mostafa, G. (2005) Refractory Periods and Climate Forcing in Cholera Dynamics. Nature, 436, 696-700.
https://doi.org/10.1038/nature03820
[58]  Focus Adriano, L., Nazir, A. and Uwishema, O. (2023) The Devastating Effect of Cyclone Freddy amidst the Deadliest Cholera Outbreak in Malawi: A Double Burden for an Already Weak Healthcare System-Short Communication. Annals of Medicine and Surgery, 85, 3761-3763.
https://doi.org/10.1097/MS9.0000000000000961
[59]  Waters, N. (2017) Tobler’s First Law of Geography.
https://doi.org/10.1002/9781118786352.wbieg1011
[60]  Koelle, K. (2009) The Impact of Climate on the Disease Dynamics of Cholera. Clinical Microbiology and Infection, 15, 29-31.
https://doi.org/10.1111/j.1469-0691.2008.02686.x
[61]  Li, Z. (2020) Multiscale Geographically Weighted Regression: Computation, Inference, and Application.

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