An An Approach to Measure the Death Impact of Covid-19 in Jakarta using Autoregressive Integrated Moving Average (ARIMA)

Abstract

Coronavirus disease 2019 (COVID-19) is a pandemic in more than 200 countries around the world. As the fourth most populous nation in the world, Indonesia is predicted to face a big threat to this pandemic particularly Jakarta as the epicenter of the virus in Indonesia. However, the nature of COVID-19 that can easily spread and also many undetected cases that do not present symptoms make it more difficult to determine the real mortality effects of COVID-19.The deaths in Jakarta from the new coronavirus may be higher than officially reported. To overcome this issue, this paper will provide an approach to measure the death impact of COVID-19 using the Autoregressive Integrated Moving Average model (ARIMA). The model will predict the ‘what if’ normal condition of the number of funerals in Jakarta compared to the real situation in March 2020 as an approach of the actual effect of COVID-19 in Jakarta. This research revealed a discrepancy of 450-1070 funerals in March 2020 that could not be predicted by the ARIMA model. This funeral gap, a forecast error, could be an approach to the potential number of possible death impacts of COVID-19 in Jakarta that should be significantly higher than the report. The people should be more conscious and alert of COVID-19 situation.

Modeling Social, Health, and Vaccines Intervention in Time of Covid-19 Pandemic Impacted in Jakarta - Indonesia

“…fection rate at Wuhan, which predicted that up to 87% was unascertained (Hao et al, 2020). Another study using Autoregressive Integrated Moving Averages Model (ARIMA) predicted that undetected cases in Jakarta varies between 6-12 % (Fadly & Sari, 2020). Based on this simulation, the number of undetected positive cases was predicted 3,960 persons (77.63% of total cases) or around 3.47 times of detected cases.…”

Spatio-Temporal Model for Predicting Covid19 Cases in Indonesia

“…This model is suitable for explaining univariate time series data. To perform ARIMA model, some steps are applied, starting from fitting an appropriate model, estimating model parameters for the chosen orders and verifying the model [8,13].…”

“…In those areas, time series model has been applied extensively since it is very beneficial as it could predict actual and future cases. Number of forecasting in epidemiology for several cases such as influenza and dengue has been conducted using ARIMA model [8].…”

“…Also, the Spatio-temporal analysis can explore the existence of relationship between factors in the data based on area in which the factors were recorded [7]. Meanwhile, the temporal part can explain the actual condition of the disease counted over the period of the outbreak [8].…”

“…Moreover, low numbers of testing also become a supporting factor of increasing trend of the confirmed cases. Indonesia may also face a threat regarding vast quantity of underreported cases which can lead to improper policy [8,9]. Furthermore, most cases show that some people who carry the disease do not have particular symptoms.…”