Assessing the impact of non-pharmaceutical interventions (NPI) on the dynamics of COVID-19: A mathematical modelling study in the case of Ethiopia

Ejigu, Bedilu Alamirie; Asfaw, Manalebish Debalike; Cavaleri, Lisa; Abebaw, Tilahun; Nanyingi, Mark; Baylis, Matthew

doi:10.1101/2020.11.16.20231746

Cited by 4 publications

(1 citation statement)

References 33 publications

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“… [29] introduce the effect of physical distancing, hand washing and face masks wearing

, with

, where

is the proportion wearing face mask,

is the efficacy of the face mask,

the proportion practicing physical distancing,

is the proportion implementing hand washing. It has been shown that

is particularly sensitive to social/physical distancing and hand washing in this case [32] . On the other hand, assume

[33] , where

is the initial contact rate,

is the final contact rate, and

is the exponential decreasing contact rate.…”

Section: Numerical Simulationsmentioning

confidence: 82%

Impact of environmental transmission and contact rates on Covid-19 dynamics: A simulation study

Rwezaura

Tchoumi

Tchuenche

2021

Informatics in Medicine Unlocked

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The emergence of the COVID-19 pandemic has been a major social and economic challenge globally. Infections from infected surfaces have been identified as drivers of Covid-19 transmission, but many epidemiological models do not include an environmental component to account for indirect transmission. We formulate a deterministic Covid-19 model with both direct and indirect transmissions. The computed basic reproduction number represents the average number of secondary direct human-to-human infections, and the average number of secondary indirect infections from the environment. Using Partial Rank Correlation Coefficient, we compute sensitivity indices of the basic reproductive number . As expected, the most significant parameter to reduce initial disease transmission is the natural death rate of pathogens in the environment. Variation of the basic reproduction number for different values of direct and indirect transmissions are numerically investigated. Decreasing the effective direct human-to-human contact rate and indirect transmission from human-to-environment will decrease the spread of the disease as decreases and vice versa. Since the effective contact rate often accounted for as a factor of the force of infection and other interventions measures such as treatment rate are prominent features of infectious diseases, we consider several functional forms of the incidence function, and numerically investigate their potential impact on the long-term dynamics of the disease. Simulations results revealed some differences for the time and infection to reach its peak. Thus, the choice of the functional form of the force of infection should mainly be influenced by the specifics of the prevention measures being implemented.

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“… [29] introduce the effect of physical distancing, hand washing and face masks wearing

, with

, where

is the proportion wearing face mask,

is the efficacy of the face mask,

the proportion practicing physical distancing,

is the proportion implementing hand washing. It has been shown that

is particularly sensitive to social/physical distancing and hand washing in this case [32] . On the other hand, assume

[33] , where

is the initial contact rate,

is the final contact rate, and

is the exponential decreasing contact rate.…”

Section: Numerical Simulationsmentioning

confidence: 82%

Impact of environmental transmission and contact rates on Covid-19 dynamics: A simulation study

Rwezaura

Tchoumi

Tchuenche

2021

Informatics in Medicine Unlocked

View full text Add to dashboard Cite

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Assessing the Accuracy of Early COVID-19 Case and Fatality Model Projections in Africa

Mabuka¹,

Craig²,

Schueller³

et al. 2022

Preprint

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ObjectiveWe compared reported COVID-19 case, fatality, and peak date data for Africa Union (AU) member states with estimates and projections produced by various mathematical models to assess their accuracy in the context of an ongoing pandemic and identify key gaps to improve the utility of models in the future.MethodsWe conducted a systematic literature review to identify studies published in any language between January and December 2020 that reported results of COVID-19 modeling analyses for any AU member state. Reported COVID-19 case, fatality, peak date, and testing rate data were obtained. Descriptive, bivariate, and meta-analyses were conducted to compare reported data to model-generated estimates. FindingsFor included countries in the respective model simulation periods, model-predicted cumulative cases ranged from 2 to 76,213,155 while model-predicted cumulative deaths ranged from 8 to 700,000. The difference between reported and predicted cumulative COVID-19 cases was between -99.3 % to 1.44×106 % with most values being above 24.7%, and the difference between reported and predicted cumulative COVID-19 deaths for models reviewed was between -2.0 % to 2.73×105 % with most values being above 50.0%. The difference in the predicted and reported dates for the first epidemic wave peak was between -242 Days to 249 Days.ConclusionFor the first COVID-19 epidemic wave, epidemiological model results were observed to have high precision but low accuracy when compared to reported peak case date and cumulative cases and deaths indicating that these data were either under-reported or model-overestimated.

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Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022

Gerasimenko

2022

City Healthcare

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast. Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country. Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed. Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.

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Assessing the impact of non-pharmaceutical interventions (NPI) on the dynamics of COVID-19: A mathematical modelling study in the case of Ethiopia

Cited by 4 publications

References 33 publications

Impact of environmental transmission and contact rates on Covid-19 dynamics: A simulation study

Impact of environmental transmission and contact rates on Covid-19 dynamics: A simulation study

Assessing the Accuracy of Early COVID-19 Case and Fatality Model Projections in Africa

Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022

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