Data model to predict prevalence of COVID-19 in Pakistan
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Abstract
Coronavirus disease 2019 (COVID19) has spread to 181 countries and regions and leaving behind 1,133,788 confirmed cases and 62784 deaths worldwide. Countries with lower health services and facilities like Pakistan are on great risk. Pakistan so far has 3,277 confirmed cases and 50 reported deaths due to COVID19. Various mathematical models had presented to predict the global and regional size of pandemic. However, all those models have certain limitation due to their dependence on different variables and analyses are subject to potential bias. As each country has its own dimension therefore country specific model are required to develop accurate estimate. Here we present a data model to predict the size of COVID19 in Pakistan. In this mathematical data model, we used the time sequence mean weighting (TSMW) to estimate the expected future number of COVID19 cases in Pakistan until 29 th April 2020. For future expected numbers of cases for long terms the data collection have to be maintained in real time.
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SciScore for 10.1101/2020.04.06.20055244: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
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:- Th…
SciScore for 10.1101/2020.04.06.20055244: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
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.
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