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The COVID-19 Pandemic in Zimbabwe: A Spatial and Temporal Perspective

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The COVID-19 - Health Systems Nexus

Abstract

Numerous pandemics have ravaged the world since prehistoric times. Some of the prevalent pandemics include the flu pandemic (1889–1890), Spanish flu (1918–1920), AIDS (1981–present) and the H1N1 swine flu pandemic (2009–2010). As pandemics occur, their incidence and prevalence rates are spatially heterogeneous, and the spatial distributions change over time. Understanding the national spatial distribution, temporal epidemic trends and transmission patterns of COVID-19 contributes to providing timely information for the national response to the pandemic. This chapter examines the Zimbabwean COVID-19 pandemic spatial patterns and temporal trends to enable decision-makers to prioritise vulnerable regions and plan appropriately as informed by the temporal patterns. A quantitative research design was adopted, which involved statistical computations and mapping of COVID-19 variable data obtained from web-based repositories. Results indicate spatial variability in the proliferation of COVID-19, with the most populated areas experiencing higher infection rates. There is a significant increase in the monthly new infections and new deaths, while the cumulative infection rates show some phases of steep increase. The case fatality rate has flattened. Policymakers and stakeholders must design strategies that respond to the spatial and temporal trends for optimal containment of the pandemic.

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References

  • Adegboye, O. A., Leung, D. H., & Wang, Y. G. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67(2), 329–354.

    Google Scholar 

  • Adegboye, O. A., Adekunle, A. I., Pak, A., Gayawan, E., Leung, D. H., Rojas, D. P., Elfaki, F., McBryde, E. S., & Eisen, D. P. (2021). Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study. Travel Medicine and Infectious Disease, 40, 101988.

    Article  Google Scholar 

  • Adekunle, A. I., Adegboye, O. A., Gayawan, E., & McBryde, E. S. (2020). Is Nigeria really on top of COVID-19? (Vol. 148, pp. 1–7). Message from effective reproduction number.

    Google Scholar 

  • Alam, M. Z. (2021). Is population density a risk factor for communicable diseases like COVID-19? A case of Bangladesh. Asia Pacific Journal of Public Health, 1, 11–22.

    Google Scholar 

  • Arab-Mazar, Z., Sah, R., Rabaan, A. A., Dhama, K., & Rodriguez-Morales, A. J. (2020). Mapping the incidence of the COVID-19 hotspot in Iran–Implications for Travellers. Travel Medicine and Infectious Disease, 34, 101630.

    Article  Google Scholar 

  • Aziz, P. Y., Hadi, J. M., Aram, M. S., Aziz, S. B., Rahman, H. S., & Ahmed, H. A. (2020). The strategy for controlling COVID-19 in Kurdistan Regional Government (KRG)/ Iraq: Identification, epidemiology, transmission, treatment, and recovery. International Journal Surgery, 25, 41–46.

    Google Scholar 

  • Banks, R. B. (2014). Growth and diffusion phenomena: Mathematical frameworks and applications. Springer-Verlag.

    Google Scholar 

  • Benvenuto, D., Giovanetti, M., Ciccozzi, A., Spoto, S., Angeletti, S., & Ciccozzi, M. (2020). The 2019-new coronavirus epidemic: Evidence for virus evolution. Journal of Medical Virology, 92(4), 455–459.

    Article  Google Scholar 

  • Bhadra, A., Mukherjee, A., & Sarkar, K. (2021). Impact of population density on COVID-19 infected and mortality rate in India. Modeling Earth Systems and Environment, 7, 623–629.

    Article  Google Scholar 

  • Chapungu, L., & Nhamo, G. (2021). Interfacing vector-borne disease dynamics with climate change: Implications for the attainment of SDGs in Masvingo City, Zimbabwe. Jàmbá: Journal of Disaster Risk Studies, 13(1), a1175. https://doi.org/10.4102/Jamba.v13i1.1175

    Article  Google Scholar 

  • Chen, Y. G. (2019). Fractal dimension analysis of urban morphology based on spatial correlation functions. In L. D’Acci (Ed.), Mathematics of urban morphology (pp. 21–53). Springer Nature Switzerland AG.

    Chapter  Google Scholar 

  • Coelho, M. T. P., Rodrigues, J. F. M., Medina, A. M., Scalco, P., Terribile, L. C., Vilela, B., Diniz-Filho, J. A. F., & Dobrovolski, R. (2020). Global expansion of COVID-19 pandemic is driven by population size and airport connections. PeerJ. Available on https://doi.org/10.7717/peerj.9708. Accessed on 12-01-2022.

  • El-Sadr, W. M., & Justman, J. (2020). Africa in the path of Covid-19. New England Journal of Medicine, 383(3), e11.

    Article  Google Scholar 

  • Fedele, D., & Francesco, A. (2021). Obesity, malnutrition, and trace element deficiency in the coronavirus disease (COVID-19) pandemic. Nutrition, 81, 111–116.

    Article  Google Scholar 

  • Forman, R., Atun, R., McKee, M., & Mossialos, E. (2021). ‘12 Lessons learned from the management of the coronavirus pandemic’. Health Policy, 124, 577–580. https://doi.org/10.1016/j.healthpol.2021.05.008. Accessed 27 December 2021.

  • Gayawan, E., Awe, O. O., Oseni, B. M., Uzochukwu, I. C., Adekunle, A., Samuel, G., Eisen, D. P., & Adegboye, O. A. (2020a). The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa. Epidemiology and Infection, 148, e212. https://doi.org/10.1017/S0950268820001983

    Article  Google Scholar 

  • Gayawan, E., Fasusi, O. D., & Bandyopadhyay, D. (2020b). Structured additive distributional zero augmented beta regression modeling of mortality in Nigeria. Spatial statistics, 35, 100415.

    Article  Google Scholar 

  • Gilbert, M., Pullano, G., Pinotti, F., Valdano, E., Poletto, C., Boëlle, P. Y., d’Ortenzio, E., Yazdanpanah, Y., Eholie, S. P., Altmann, M., & Gutierrez, B. (2020). Preparedness and vulnerability of African countries against importations of COVID-19: A modelling study. The Lancet, 395(10227), 871–877.

    Article  Google Scholar 

  • Gramsone, K. (2020). Meteorological and climatic factors and COVID-19. International Journal of Epidemiology, 10(2), 400–417.

    Google Scholar 

  • Guner, R., Hasanoglu, I., & Aktas, F. (2020). COVID-19: Prevention and control measures in community. Turk Journal Medical Science, 50(3), 571–577.

    Article  Google Scholar 

  • Hannah R., Edouard M., Lucas R., Cameron A., Charlie G., Esteban O., Joe H., Bobbie M., Diana B. and Max R. (2022). Coronavirus Pandemic (COVID-19). Available at https://ourworldindata.org/coronavirus, Accessed on 10-01-2022

  • Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., & Hu, Y. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 395, 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5

    Article  Google Scholar 

  • Kadi, N., & Khelfaoui, M. (2020). Population density, a factor in the spread of COVID-19 in Algeria: Statistic study. Bulletin of the National Research Centre, 44, 138.

    Article  Google Scholar 

  • Kang, D., Choi, H., Kim, J. H., & Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 94, 96–102.

    Article  Google Scholar 

  • Kocsis, L., Usman, A., Jourdan, A.-L., Hassan, S. H., Jumat, N., Daud, D., Briguglio, A., Slik, F., Rinyu, L., Futo, I., (2020). The Bruneian record of “Borneo Amber”: A regional review of fossil tree resins in the Indo-Australian Archipelago. Earth-Science Reviews, 201(103005), 1–21. Available at https://doi.org/10.1016/j.earscirev.2019.103005. Accessed 30 December 2021

  • Lu, Q., & Shi, Y. (2020). Coronavirus disease (COVID-19) and neonate: What neonatologist need to know. Journal of Medical Virology, 92(6), 564–567.

    Article  Google Scholar 

  • Lu, H., Stratton, C. W., & Tang, Y. W. (2020). Outbreak of pneumonia of unknown etiology in Wuhan China: The mystery and the miracle. Journal of Medical Virology, 92(4), 401–420.

    Article  Google Scholar 

  • Martinez-Alvarez, M., Jarde, A., Usuf, E., Brotherton, H., Bittaye, M., Samateh, A. L., Antonio, M., Vives-Tomas, J., D’Alessandro, U., & Roca, A. (2020). COVID-19 pandemic in West Africa. The Lancet Global Health, 8(5), e631–e632.

    Article  Google Scholar 

  • Mann, H. B. (1945). Non-Parametric Test against Trend. Econometrica, 13, 245–259. Available at https://dx.doi.org/10.2307/1907187. Accessed 28 December 2021.

  • Mohammed, A., Sha’aban, A. I., Jatau, I., Yunusa, A. M., Isa, A. S., Wada, K., Obamiro, H., & Zainal, B. (2021). Assessment of COVID-19 information overload among the general public. Journal of Racial and Ethnic Health Disparities, 1–9, https://doi.org/10.1007/s40615-020-00942-0

  • Oliveiros, B., Caramelo, L., Ferreira, N. C., & Caramelo, F. (2020). Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases. MedRxiv.

    Google Scholar 

  • Sajadi, P., Habibzadeh, A., Vintzileos, S., Shokouhi, F., & Miralles-Wilhelm, A. (2020). Amoroso temperature and latitude analysis to predict potential spread and seasonality for COVID-19. Available at SSRN 3550308, Accessed 10 Jan 2022.

    Google Scholar 

  • Shahzad, K., Farooq, T. H., Doğan, B., Zhong, H. L., & Shahzad, U. (2021). Does environmental quality and weather induce COVID-19: Case study of Istanbul Turkey. Environmental Forensics, 1–12.

    Google Scholar 

  • Tehran, S., Khabiri, N., Moradi, H., Mosavat, M. S., & Khabiri, S. (2021). Evaluation of vitamin D levels in COVID-19 patients referred to Labafinejad hospital in Tehran and its relationship with disease severity and morality. Clin Nutr ESPEN, 42, 313.

    Google Scholar 

  • Walter, S. D. (2000). Disease mapping: A historical perspective: Spatial epidemiology methods and applications. Oxford University Press.

    Google Scholar 

  • Wang, H., Zhang, Y., Lu, S., & Wang, S. (2020a). Tracking and forecasting milepost moments of the epidemic in the early-outbreak: Framework and applications to the COVID-19. F1000Research, 9.

    Google Scholar 

  • Wang, J., Tang, K., Feng, K., & Lv, W. (2020b). High temperature and high humidity reduce the transmission of COVID-19. Available at SSRN 3551767. Accessed 09 Jan 2022.

    Google Scholar 

  • Wang, Y., Liu, Y., Struthers, J., & Lian, M. (2021). Spatiotemporal characteristics of the COVID-19 epidemic in the United States. Clinical Infectious Diseases, 72(4), 643–651. https://doi.org/10.1093/cid/ciaa934

    Article  Google Scholar 

  • Wong, D. W. S., & Li, Y. (2020). Spreading of COVID-19: Density matters. Public Library of Science ONE, 15, 200–212.

    Google Scholar 

  • World Health Organisation (WHO). (2020). Coronavirus disease (COVID-19) Pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 27 December 2021.

  • Wuhan Municipal Health and Health Commission. (2020). Wuhan Municipal Health and Health Commission’ Briefing on current pneumonia epidemic situation in our city. Available on http://www.wuhan.gov.cn/front/web/showDetail/2019123108989. Accessed 02 Jan 2022.

  • Yan, Y., Chen, H., Chen, L., Cheng, B., Diao, P., & Dong, L. (2020). Consensus of Chinese experts on protection of skin and mucous membrane barrier for health-care workers fighting against coronavirus disease 2019. Dermatology Therapy Journal, 33(4), 133–140.

    Google Scholar 

  • Zhong, P., Guo, S., & Chen, T. (2020). Correlation between travellers departing from Wuhan before the spring festival and subsequent spread of COVID-19 to all provinces in China. Journal of Travel Medicine, 27(3), 260–373.

    Article  Google Scholar 

  • Zhu, M., Kleepbua, J., Guan, Z., Chew, S. P., Tan, J. W., Shen, J., Latthitham, N., Hu, J., Law, J. X., & Li, L. (2021). Early spatiotemporal patterns and population characteristics of the COVID-19 pandemic in Southeast Asia. Healthcare, Available at https://doi.org/10.3390/healthcare9091220. Accessed 05 Jan 2022.

  • ZimStats. (2022). Monitoring COVID-19 impact on households. Zimbabwe Data Portal. https://zimbabwe.opendataforafrica.org/wadppcg/monitoring-covid-19-impact-on-households. Accessed 23 Feb 2022.

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Correspondence to Evans Chazireni .

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Chazireni, E., Chapungu, L., Nhamo, G. (2023). The COVID-19 Pandemic in Zimbabwe: A Spatial and Temporal Perspective. In: Chapungu, L., Chikodzi, D., Dube, K. (eds) The COVID-19 - Health Systems Nexus. Global Perspectives on Health Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-21602-2_2

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