A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support

With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

Reducing bias in risk indices for COVID-19

Spatiotemporal modelling of infectious diseases such as coronavirus disease 2019 (COVID-19) involves using a variety of epidemiological metrics such as regional proportion of cases and/or regional positivity rates. Although observing changes of these indices over time is critical to estimate the regional disease burden, the dynamical properties of these measures, as well as crossrelationships, are usually not systematically given or explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk estimate that is biased neither by variation in population size nor by the spatial heterogeneity of testing. In particular, the proposed methodology would be useful for unbiased identification of time periods with elevated COVID-19 risk without sensitivity to spatial heterogeneity of neither population nor testing coverage.We offer a case study in Poland that shows improvement over the bias of currently used methods. Our results also provide insights regarding regional prioritisation of testing and the consequences of potential synchronisation of epidemics between regions. The approach should apply to other infectious diseases and other geographical areas.

COVID-19 vaccine prioritization based on district classification in Yogyakarta Province, Indonesia

Due to limited availability, Indonesia’s coronavirus disease 2019 (COVID-19) vaccination will be done in 4 stages until herd immunity has been reached. Yogyakarta, an education and tourist destination, needs to get a specific, spatial estimation of the exact need for COVID-19 vaccination without delay. This study sheds light on identifying which districts should be prioritized at each vaccination phase. Secondary data collected from provincial, and county-level statistical agencies were quantitatively calculated by the Z-Score method. The results indicate that the first phase of vaccination should prioritize Pengasih and Sentolo districts in Kulon Progo Regency, which have a large number of health workers; the districts of Depok, Banguntapan, Piyungan, Sewon, Wonosari, Gamping, Mlati and Ngaglik should be done in the second phase based on the fact that these districts have many public service officials as well as elderly people; Umbulharjo and Depok districts will be approached in the third phase since they have more vulnerable groups and facilities that may promote COVID- 19 transmission during their daily activities; while the fourth phase should focus on the districts of Banguntapan, Sewon, Kasihan, Gamping, Mlati, Depok, and Ngaglik due to the intensity of COVID-19 clusters discovered there. Overall, vaccination would be given the priority in the districts with the largest number of people in need, i.e., public service officers, elderly people and those likely to be exposed to the coronavirus causing COVID-19.

Vaccine hesitancy in East Malaysia (Sabah): A survey of the national COVID-19 immunisation programme

The Malaysian government has introduced the National COVID-19 Immunisation Programme (PICK) as a new mechanism to address the transmission of coronavirus disease 2019 (COVID-19). Unfortunately, the number of PICK registrations is still unsatisfactory and is now even lower. The low level of participation of the Sabah (East Malaysia) population significantly impacts the PICK registrations. Therefore, this study aims to identify the factors that cause vaccine hesitancy among the people of Sabah. This study seeks to identify these trends based on zone and district boundaries. A total of 1024 respondents were sampled in this study. Raw data collected through the survey method were analysed using K-means clustering, principal component analysis (PCA), and spatial analysis. The study discovered that factors including confidence, authority, mainstream media, complacency, social media, and convenience are the top causes of vaccine hesitancy among respondents. This study also revealed that the Sabah population’s key variables causing vaccine hesitancy to vary by region (zones and districts). The conclusion is significant as a source of supporting data for stakeholders seeking to identify the Sabah population’s constraints in each region and therefore, it would help improve PICK management’s performance in Sabah.

Vulnerability to COVID-19 in Pernambuco, Brazil: A geospatial evaluation supported by multiple-criteria decision aid methodology

The paper presents an innovative application to identify areas vulnerable to coronavirus disease 2019 (COVID-19) considering a combination of spatial analysis and a multi-criteria learning approach. We applied this methodology in the state of Pernambuco, Brazil identifying vulnerable areas by considering a set of determinants and risk factors for COVID-19, including demographic, economic and spatial characteristics and the number of human COVID-19 infections. Examining possible patterns over a set number of days taking the number of cases recorded, we arrived at a set of compatible decision rules to explain the relation between risk factors and COVID-19 cases. The results reveal why certain municipalities are critically vulnerable to COVID-19 highlighting locations for which knowledge can be gained about environmental factors.

Association of city-level walkability, accessibility to biking and public transportation and socio-economic features with COVID-19 infection in Massachusetts, USA: An ecological study

With people restricted to their residences, neighbourhood characteristics may affect behaviour and risk of coronavirus disease 2019 (COVID-19) infection. We aimed to analyse whether neighbourhoods with higher walkability, public transit, biking services and higher socio-economic status were associated with lower COVID-19 infection during the peak of the COVID-19 pandemic in Massachusetts. We used Walk Score®, Bike Score®, and Transit Score® indices to assess the walkability and transportation of 72 cities in Massachusetts, USA based on availability of data and collected the total COVID-19 case numbers of each city up to 10 April 2021. We used univariate and multivariate linear models to analyse the effects of these scores on COVID-19 cases per 100,000 in each city, adjusting for demographic covariates and all covariates, respectively. In the 72 cities studied, the average Walk Score, Transit Score and Bike Score was 48.7, 36.5 and 44.1, respectively, with a total of 426,182 COVID-19 cases. Higher Walk Score, Transit Score, and Bike Score rankings were negatively associated with COVID-19 cases per 100,000 persons (<0.05). Cities with a higher proportion of Hispanic population and a lower median household income were associated with more COVID-19 cases per 100,000 (P<0.05). Higher Walk Score, Transit Score and Bike Score were shown to be protective against COVID-19 transmission, while socio-demographic factors were associated with COVID-19 infection. Understanding the complex relationship of how the structure of the urban environment may constrain commuting patterns for residents and essential workers during COVID-19 would offer potential insights on future pandemic preparedness and response.

Development of a public geographical information system-based website to follow the impact of control activities of soil-transmitted helminths in endemic countries

Soil-transmitted helminth (STH) infections are among the most common neglected tropical diseases worldwide causing high morbidity and mortality rates in endemic areas. Preventive chemotherapy (PC) programmes and health education are recommended by the World Health Organization (WHO) to reduce the impact of STH in endemic countries. Following our role as WHO collaborating centre (WHO CC ITA-116), we have developed a WebGIS and a dataset to support PC programmes to monitor the impact of STH control. This vHealth presentation shows the potentiality of these tools in improving communication among WHO’s regional and country offices, Ministries of Health, pharmaceutical industries and other partners.

Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis

Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.

Evaluating the spatial and temporal patterns of the severe fever thrombocytopenia syndrome in Republic of Korea

Severe fever with thrombocytopenia syndrome (SFTS) is a new infectious disease with a high mortality rate and increased incidence in Republic of Korea since the first case was reported in 2013. The average mortality rate varies by region and year but remains high in Asia. This study aimed to evaluate the spatial and temporal patterns of SFTS cases reported to the national Disease Control and Prevention Agency (KDCA). We analysed the spatial and temporal distribution of SFTS and observed changes in areas vulnerable to the disease. We analysed data concerning 1086 confirmed SFTS patients from 2013 to 2019 categorized according to the 247 district level administrative units. To better understand the epidemiology of SFTS, we carried out spatiotemporal analyses on a yearly basis and also calculated and mapped spatial clusters of domestic SFTS by global (regional) and local Moran’s indices. To observe the annual changes in SFTS incidence rate, scan statistics for each city and district were calculated. The incidence rate showed significant clustering in specific regions, which reoccurred annually in some regions. In Republic of Korea, SFTS clusters have been expanding into the southern regions, with annual clusters concentrated between May and October. This pattern allows prediction of SFTS occurrences through spatiotemporal analysis, which makes it possible to guide measures of disease prevention.

Distribution of bovine Fasciola gigantica (Cobbold, 1885) in the district des Savanes, northern Côte d’Ivoire

Fascioliasis, caused by an infection with liver flukes of the genus Fasciola, is an important disease of livestock in most parts of the world. However, little is known about the distribution of fascioliasis in sub-Saharan Africa. We report results of a cross-sectional study conducted in 2014 in the district des Savanes in the northern part of Côte d’Ivoire. We obtained 275 livers from bovine suspected with fascioliasis and 51 unsuspected livers from 24 slaughterhouses. Livers were dissected using a standard operating procedure and all Fasciola gigantica flukes were removed from the tissues of the liver and the biliary ducts. We found F. gigantica in 125 livers from bovines suspected with fascioliasis (45.5%) in 10 departments of the district des Savanes. Among the unsuspected livers, five were positive for F. gigantica (9.8%). The distribution of fascioliasis showed considerable spatial heterogeneity, both at regional (ranging from 18.0% to 52.3%) and departmental level (ranging from 14.3% to 64.0%). Poro region was the most affected (52.3%) with a relatively homogeneous distribution. The departments most affected by fascioliasis were M’Bengué (64.0%), Sinématiali (62.1%) and Ferkessédougou (52.9%). Our study confirms that fascioliasis is an important veterinary disease in the northern part of Côte d’Ivoire, and hence, high-risk areas need to be targeted for prevention and control measures.