System Dynamics Model of Possible Covid-19 Trajectories Under Various Non-Pharmaceutical Intervention Options in a Low Resource Setting

  1. Kivuti-Bitok W Lucy
  2. Momodu S.Abiodun
  3. Cheptum Jebet Joyce
  4. Kimemia Fredrick
  5. Gichuki Isaac
  6. Ngune Irene

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

We present a population-based System Dynamics Model (SDM) of possible Covid-19 trajectories under various intervention options in the uniqueness of Kenya. We developed a stock and flow based SDM. We parametrized the SDM using published data and where data was not available, expert opinion was sought. Following validation test, the model was simulated to determined possible outcomes of non-pharmaceutical interventions in management of Covid-19. We simulate the possible impact of; social distancing, quarantining, curfew and cross-county travel restriction, lockdown of selected cities in Kenya and quarantining. We varied interventions in terms of start dates, duration of implementation and effectiveness of the interventions. We estimated the outcomes in terms of number of possible infections, recoveries and deaths. With the current state of interventions, we estimated a peak of Covid-19 in September 2020 with an estimated 13.5 Million Covid-19 cases and 33.8 thousand deaths in Kenya. The largest possible reduction in infections and mortality was achievable through increase in the effectiveness of the interventions. The suggested interventions would delay the epidemic peak of Covid-19 to between late Nov 2020 and early December 2020 with an estimated13M cases a 500 thousand reduction in Covid-19 cases and 32.4 deaths (a reduction in 1400 deaths). We conclude that SDM enables understanding of the complexity and impact of different interventions scenarios of Covid-19 in Kenya.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.06.20204487: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    4.2 Limitations of the study: Covid-19 is dynamic and the data may vary drastically. Our model is based on person to person contact and provides suggestions that take into account the current situation in the country. The application of the model may be limited to Kenya because the mixing patterns of individuals may differ in other regions and countries and across cultures. While we acknowledge sufficient data was used to populate the model, we also leave room for incorporating new knowledge to further refine the model. We also did not classify the severity of Covid-19 cases.This model does not attempt to predict the course of Covid-19 in Kenya but rather generates hypothesis as to possible Covid-19 Trajectories from possible non-pharmaceutical interventions.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.10.06.20204487v1 on medRxiv