[Paper Review] Critical data analysis of COVID-19 spreading in Indonesia to measure the readiness of new-normal policy

Muhammad Ariful Furqon, Nina Fadilah Najwa+3 more|arXiv (Cornell University)|Nov 16, 2020

COVID-19 epidemiological studies14 references4 citations

TL;DR

This study analyzes COVID-19 transmission data in Indonesia using statistical and forecasting models to evaluate the country's readiness for the new-normal policy following large-scale social restrictions. It finds a more than twofold increase in new daily cases over the past month, with growth rates outpacing predictions, signaling insufficient preparedness and urging policy reconsideration to balance economic and public health priorities.

ABSTRACT

COVID-19 pandemic has become a global issue nowadays. Various efforts have been made to break the chain of the spread of the COVID-19. Indonesia's government issued a large-scale social restrictions policy to prevent the spread of the COVID-19. However, large-scale social restrictions policy impacted the economy of the Indonesian. After several considerations, the Indonesian government implemented a new-normal policy, which regulates the activities outside the home with strict health protocols. This study's objective is to measure Indonesia's readiness level after the large-scale social restrictions period towards the new-normal period. To specify the readiness level, the measurement parameters required in the form of statistical analysis and forecasting modeling. Based on the results of statistical analysis and forecasting, over the past month, new confirmed cases increased more than two times. Besides, the growth rate of new confirmed cases dramatically increased rapidly compared to the prediction results. Therefore, the government must review the new-normal policy again and emphasize economic factors and think about health factors

Motivation & Objective

• To evaluate Indonesia’s preparedness for the new-normal policy after large-scale social restrictions.
• To identify key statistical and predictive indicators of COVID-19 transmission trends in Indonesia.
• To assess whether the new-normal policy is sustainable given current epidemiological data.
• To balance economic recovery with public health safety through data-driven policy evaluation.
• To provide evidence-based recommendations for policy adjustments based on real-time data analysis.

Proposed method

• Statistical analysis of daily confirmed COVID-19 cases in Indonesia over a recent one-month period.
• Application of forecasting modeling to project future case trends based on historical data.
• Comparison of actual case growth rates with predicted model outputs to assess accuracy and deviation.
• Use of time-series data to detect anomalies and rapid growth patterns in transmission.
• Integration of economic and health factors into policy evaluation framework.
• Evaluation of policy readiness through quantitative deviation from forecasted trends.

Experimental results

Research questions

• RQ1 To what extent has the number of new daily confirmed COVID-19 cases increased in the past month in Indonesia?
• RQ2 How do actual case growth rates compare to forecasted trends in the post-lockdown period?
• RQ3 What indicators suggest that Indonesia may not be ready for the new-normal policy?
• RQ4 How can statistical and forecasting models inform policy decisions during a pandemic transition?
• RQ5 What trade-offs between economic activity and public health are revealed by the data?

Key findings

• Over the past month, the number of new daily confirmed COVID-19 cases in Indonesia increased by more than two times.
• The actual growth rate of new confirmed cases rose more rapidly than predicted by the forecasting model.
• The deviation between actual and predicted case growth indicates a significant underestimation of transmission dynamics.
• The findings suggest that Indonesia’s current new-normal policy may not be adequately prepared for sustained transmission risks.
• The study concludes that the government must reassess the new-normal policy with stronger emphasis on health safety measures.
• Economic considerations should not override public health preparedness, as data shows rising transmission trends.