Spatio-temporal evolution of the COVID-19 across African countries

Naffeti, Bechir; Bourdin, Sébastien; Aribi, Walid Ben; Kebir, Amira; Miled, Slimane Ben

doi:10.3389/fpubh.2022.1039925

Cited by 5 publications

(3 citation statements)

References 38 publications

(42 reference statements)

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“…The proportion of vaccines vaccinated with one, two, and three doses differ Mathematical models have become a good tool for predicting the development of the COVID-19 pandemic and helping to make adequate management decisions to contain the pandemic. Various models have been developed [37][38][39]. However, each of them had some drawbacks, such as the impossibility of taking into account all the factors that negatively or positively affect the development of the COVID-19 epidemic process, that does not take into account the heterogeneity of the human population and differences in the structure of the population in different territories, etc.…”

Section: Discussionmentioning

confidence: 99%

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Chumachenko,

Dudkina,

Yakovlev

et al. 2023

International Journal of Telemedicine and Applications

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This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.

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Section: Discussionmentioning

confidence: 99%

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Chumachenko,

Dudkina,

Yakovlev

et al. 2023

International Journal of Telemedicine and Applications

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“…More importantly, SARS-CoV-2 has heterogeneously affected African countries [1] . Tunisia, Senegal, and Madagascar reported 1 , 152 , 483, 86 , 594, and 66 , 098 confirmed cases and 29 , 378, 1968, and 1403 deaths, respectively, by April 23, 2023.…”

Section: Introductionmentioning

confidence: 99%

Comparative reconstruction of SARS-CoV-2 transmission in three African countries using a mathematical model integrating immunity data

Naffeti,

BenAribi,

Kebir

et al. 2024

IJID Regions

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“…More importantly, SARS-CoV-2 has heterogeneously a ected African countries (Na eti et al, 2022). Tunisia, Senegal, and Madagascar reported 1, 152, 483, 86, 594, and 66, 098 confirmed cases and 29, 378, 1968, and 1403deaths, respectively, by April 23, 2023.…”

Section: Introductionmentioning

confidence: 99%

Comparative Reconstruction of SARS-CoV-2 transmission in three African countries using a mathematical model integrating immunity data

Naffeti,

BenAribi,

Kebir

et al. 2023

Preprint

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Objectives: Africa has experienced fewer coronavirus disease 2019 (COVID-19) cases and deaths than other regions, with a contrasting epidemiological situation between countries, raising questions regarding the determinants of disease spread in Africa. Method: We built a susceptible-exposed-infected-recovered model including COVID-19 mortality data where recovery class is structured by specific immunization and modeled by a partial differential equation considering the opposed effects of immunity decline and immunization. This model was applied to Tunisia, Senegal, and Madagascar. Finding: Senegal and Tunisia experienced two epidemic phases. Initially, infections emerged in naive individuals and were limited by social distancing. Variants of concern (VOCs) were also introduced. The second phase was characterized by successive epidemic waves driven by new VOCs that escaped host immunity. Meanwhile, Madagascar demonstrated a different profile, characterized by longer intervals between epidemic waves, increasing the pool of susceptible individuals who had lost their protective immunity. The impact of vaccination in Tunisia and Senegal on model parameters was evaluated. Interpretation: Loss of immunity and vaccination-induced immunity have played crucial role in controlling the African pandemic. Severe acute respiratory syndrome coronavirus 2 has become endemic now and will continue to circulate in African populations. However, previous infections provide significant protection against severe diseases, thus providing a basis for future vaccination strategies.

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Spatio-temporal evolution of the COVID-19 across African countries

Cited by 5 publications

References 38 publications

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Comparative reconstruction of SARS-CoV-2 transmission in three African countries using a mathematical model integrating immunity data

Comparative Reconstruction of SARS-CoV-2 transmission in three African countries using a mathematical model integrating immunity data

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