Forecasting Number of COVID-19 Cases in Indonesia with ARIMA and ARIMAX Models

Aji, Bimo Satrio; Indwiarti,; Rohmawati, Aniq Atiqi

doi:10.1109/icoict52021.2021.9527453

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…As we can observe, both models predict the number of next day cases (1-day) with an average weekly error rate of 10–20%, and the number of cases in a week (7-days) with an average weekly error rate of 30–40%. Models’ performance is in a comparable range to previous studies [ 21 , 29 , 30 , 42 – 45 ], but with the difference that we reported the results for the entire year of 2020 and the US, not specific regions or COVID waves.…”

Section: Resultssupporting

confidence: 87%

A fairness assessment of mobility-based COVID-19 case prediction models

Erfani,

Frias-Martinez

2023

PLoS ONE

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In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

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Section: Resultssupporting

confidence: 87%

A fairness assessment of mobility-based COVID-19 case prediction models

Erfani,

Frias-Martinez

2023

PLoS ONE

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“…The best SARIMA models will perform each Google Mobility category as the external variables for the COVID-19 dataset. Similar to the SARIMA process, the best fit combination model is chosen by the lowest RMSE [9].…”

Section: Plos Onementioning

confidence: 99%

Predicting COVID-19 cases in Belo Horizonte—Brazil taking into account mobility and vaccination issues

Dias,

Diniz,

Souto

et al. 2024

PLoS ONE

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The pandemic caused millions of deaths around the world and forced governments to take drastic measures to reduce the spread of Coronavirus. Understanding the impact of social distancing measures on urban mobility and the number of COVID-19 cases allows governments to change public policies according to the evolution of the pandemic and plan ahead. Given the increasing rates of vaccination worldwide, immunization data may also represent an important predictor of COVID-19 cases. This study investigates the impact of urban mobility and vaccination upon COVID-19 cases in Belo Horizonte, Brazil using Prophet and ARIMA models to predict future outcomes. The developed models generated projections fairly close to real numbers, and some inferences were drawn through experimentation. Brazil became the epicenter of the COVID-19 epidemic shortly after the first case was officially registered on February 25th, 2020. In response, several municipalities adopted lockdown (total or partial) measures to minimize the risk of new infections. Here, we propose prediction models which take into account mobility and vaccination data to predict new COVID-19 cases.

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“…Penelitian oleh (Rachmawati, 2020) yang bertujuan untuk meramalkan penyebaran jumlah kasus Covid-19 di Provinsi Jawa Tengah telah dilakukan tetapi hanya menggunakan metode ARIMA". Penelitian lainnya yang menggunakan model ARIMAX juga telah dilakukan oleh (Aji Indwiarti, 2021) namun variabel exogen yang digunakan yaitu data google trend. Berdasarkan hasil literature review yang telah kami lakukan, penelitian mengenai pengaruh jumlah vaksinasi secara spesifik terhadap jumlah kasus Covid-19 menggunakan model ARIMAX di provinsi Sulawesi Selatan belum dilakukan.…”

Section: Pendahuluanunclassified

Temporal Analysis of the Influence of the Number of Vaccinations on the Number of Covid-19 Cases in South Sulawesi Province Using ARIMAX Model

Aswi,

Tiro,

Sudarmin

et al. 2022

IJFS

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South Sulawesi is a province that has the highest number of Covid-19 cases in the Sulawesi island region. Vaccination is one way that is considered effective in controlling the infection of a disease. Covid-19 vaccination in Indonesia was carried out in January 2021. This study aims to obtain the best Autoregressive Integrated Moving Average X (ARIMAX) model in modeling the effect of the number of vaccinations on the number of Covid-19 cases in South Sulawesi Province. Data on the number of vaccinations and Covid-19 cases in South Sulawesi Province (October 1, 2021 - January 31, 2022) were used. The best ARIMAX model in modeling Covid-19 in relation to the number of vaccinations is ARIMAX (2,1,0). The results showed that the number of vaccinations had a negative effect on the number of Covid-19 cases at the significant level of 10%. This indicates that if the number of vaccinations increases then the number of Covid-19 cases will decrease.

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Forecasting Number of COVID-19 Cases in Indonesia with ARIMA and ARIMAX Models

Cited by 10 publications

References 7 publications

A fairness assessment of mobility-based COVID-19 case prediction models

A fairness assessment of mobility-based COVID-19 case prediction models

Predicting COVID-19 cases in Belo Horizonte—Brazil taking into account mobility and vaccination issues

Temporal Analysis of the Influence of the Number of Vaccinations on the Number of Covid-19 Cases in South Sulawesi Province Using ARIMAX Model

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