COVID 19 healthcare facility demand forecasts for rural residents

  1. Andrio Adwibowo

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

One of the main challenges in dealing with the current COVID 19 pandemic is how to fulfill the healthcare facility demands especially for the residents living in the rural areas that have restricted healthcare access. Correspondingly, this study aims to record the daily COVID 19 cases and continue with the forecasting of the average daily demand (ADD) of healthcare facilities including beds, ICUs, and ventilators using ARIMA model. The forecasts were made for 3 rural populations located in the southern Amazon. The model shows that the healthcare ADD was different in each population. Likewise, the model forecasts that in a rural population that has the highest daily case with projected average cases equal to 67 cases/day (95%CI: 24, 110), that population has to fulfill healthcare ADD consisting of 57 beds/day (95%CI: 21, 93), 8 ICUs/day (95%CI: 2, 14), and 2 ventilators/day (95%CI: 2, 3). To conclude, the ARIMA model has addressed critical questions about ADD for beds, ICUs, and ventilators for rural residents. This ARIMA model based healthcare plan will hopefully provide versatile tool to improve healthcare resource allocations.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.05.20123380: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.06.05.20123380v1 on medRxiv