The COVID-19 pandemic in Indonesia started with 2 cases on March 2, 2020, and as of May 11, a total of 14 265 people were infected. The government through Task Force for COVID-19 Rapid Response informs the progress of COVID-19 pandemic in Indonesia, but no one has provided a picture of the risk distribution in all provinces in Indonesia. This research is intended to identify high-risk provinces based on risk factors in each province and to find COVID-19 hotspots. This is an ecological study that used aggregate data. We used a map to present the risk distribution in Indonesia, and Local Indicators of Spatial Association (LISA) to define the hotspot area of COVID-19 in Indonesia. There are 6 provinces identified as high-risk areas of COVID-19 in Indonesia, and the hotspot provinces are Banten, DKI Jakarta, West Java, East Java, and Central Java.

The world is currently experiencing a COVID-19 pandemic. More than 5 million people have been infected with COVID-19 and more than 300 thousand have died from this virus worldwide. In Indonesia, the number of infected people has reached more than twenty thousand people and more than one thousand people have died from this virus. During the COVID-19 pandemic, Case Fatality Rate was a very important measure for many people because death is very important to each person, including questions of when and how death will occur and whether there is any way to delay it. However, caution is needed in calculating and displaying CFR. This paper will present the uses and the weaknesses of CFR in the context of the COVID-19 pandemic in Indonesia.

Indonesia is a vast country struggling to reduce its stunting prevalence. Hence, identifying priority areas is urgent. In determining areas to prioritize, one needs to consider geographical issues, particularly correlations among areas. This study aimed to discover whether stunting prevalence in Indonesia occurs randomly or in clusters; and, if it occurs in clusters, which areas are the hotspots. This ecological study used aggregate data from the 2018 National Basic Health Research and Poverty Data and Information Report from the Statistics Indonesia. This study analyzed 514 districts/cities across 34 provinces on seven main islands in Indonesia. The method used was the Euclidean distance to define the spatial weight. Moran's index test was used to identify autocorrelation, while a Moran scatter plot was applied to identify stunting hotspots. Autocorrelation was found among districts/cities in Sumatra, Java, Sulawesi, and Bali East Nusa Tenggara West Nusa Tenggara Islands, resulting in 133 districts/cities identified as stunting hotspots on four major islands. Autocorrelation proves that stunting in Indonesia does not occur randomly.

While the national prevalence of stunting in Indonesia has decreased, the level remains high in many districts/cities and there is significant variation. This ecological study employed aggregated data from the Basic Health Research Report and the District/City Poverty Data from 2018. We investigated the determinants of stunting prevalence at the district/city level, including autocorrelation applying the spatial autoregressive (SAR) model. The analyses revealed stunting prevalence above the national average in 282 districts/cities (54.9%), i.e. ≥30% in 297 districts/cities (57.8%) and ≥40% in 91 districts/cities (17.7%). Autocorrelation was found between Sumatra, Java, Sulawesi as well as Bali, East Nusa Tenggara and West Nusa Tenggara (Bali NTT NTB). The SAR modelling revealed the following variables with significant impact on the stunting prevalence in various parts of the country: closet defecation, hand washing, at least four antenatal care visits during pregnancy, poverty, immunisation and supplementary food for children under 5 years.

Objectives. To find stunting hotspots district or cities in Indonesia in seven major islands in Indonesia. Method. This is an ecological study that using aggregate data. We used data from The Basic Health Research Report of Indonesia 2018 and The Poverty Data and Information Report from the Central Bureau of Statistics 2018. We analyzed 514 districts or cities in Indonesia that spread out in 7 major Islands with 34 provinces. We used The Euclidean distance method to determine the neighborhood. Morans test was occupied to identify autocorrelation while Morans Scatter Plot particularly in the high high quadrant was used to identify stunting hotspot areas. Result. It was found that there is autocorrelation among districts or cities in four major islands namely Sumatera, Java, Sulawesi, and Bali Nusa Tenggara Timur Nusa Tenggara Barat. We identified 135 districts or cities as stunting hotspot areas that spread in 14 provinces in four islands. Conclusion. There is autocorrelation among districts or cities in Sumatera, Java, Sulawesi, and Bali NTT NTB which resulted in 135 districts or cities identified as stunting hotspots in four major islands in Indonesia Policy implication. Provide information to the government in prioritizing stunting prevention areas in Indonesia in term of the acceleration of stunting prevention.