An emerging outbreak of a novel coronavirus, COVID-19, has now been detected in at least 211 countries worldwide. Given this pandemic situation, robust risk communication is urgently needed, particularly in affected countries. Therefore, this study explored the potential use of Google Trends (GT) to monitor public restlessness toward COVID-19 infection in Taiwan. We retrieved GT data for the specific locations and subregions in Taiwan nationwide using defined search terms related to the coronavirus, handwashing, and face masks. Searches related to COVID-19 and face masks in Taiwan rapidly increased following the announcements of Taiwan's first imported case and reached a peak as locally acquired cases were reported. However, searches for handwashing gradually increased during the period of face-mask shortage. Moreover, high to moderate correlations between Google relative search volumes (RSVs) and COVID-19 cases were found in Taipei (lag-3), New Taipei (lag-2), Taoyuan (lag-2), Tainan (lag-1), Taichung (lag0), and Kaohsiung (lag0). In response to the ongoing outbreak, our results demonstrated that GT could potentially define the proper timing and location for practicing appropriate risk communication strategies for affected populations.

Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

Background: Dengue fever is a mosquito-borne viral disease with high incidence in over 128 countries. WHO estimates 500,000 people with severe dengue are hospitalized annually and 2.5% of those affected die. Indonesia is a hyperendemic country for dengue with an increasing number of cases in the last decade. Unfortunately, the trends of Indonesian dengue research are relatively unknown. Objective: This research aimed to depict bibliographic trends and knowledge structure of dengue publications in Indonesia relative to that of South-east Asia (SEA) from 2007 to 2016. Methods: Bibliographic data were collected from PubMed filtered by Indonesia country affiliation. The annual growth rate of publication was measured and compared with neighborhood countries in the SEA region. Network analysis was used to visualize emerging research issues. Results: About 1,625 dengue-related documents originated from SEA region, of which Indonesia contributed 5.90%. The publication growth rate in Indonesia, however, is the highest in ASEAN region (28.87%). Total citations for documents published from Indonesia was 980, with an average of 14 citations per publication and h-index of 16. Within the first five years, the main research topics were related to insect vector and diagnostic method. While insect vector remained dominant in the last five years, other topics such as disease outbreak, dengue virus, and dengue vaccine started emerging. Conclusion: In the last 10 years, dengue publications’ growth from Indonesia in international journals improved significantly, despite less number of publications compared to other SEA countries. Efforts should be made to improve the quantity and quality of publications from Indonesia. The research topics related to dengue in Indonesia are in line with studies in SEA. Stakeholders and policy makers are encouraged to develop a roadmap for dengue research in the future.

Malaria has been targeted for elimination from Indonesia by 2030, with varying timelines for specific geographical areas based on disease endemicity. The regional deadline for malaria elimination for Java island, given the steady decrease of malaria cases, was the end of 2015. Purworejo District, a malaria-endemic area in Java with an annual parasite incidence (API) of 0.05 per 1,000 population in 2009, aims to enter this elimination stage. This study documents factors that affect incidence and spatial distribution of malaria in Purworejo, such as geomorphology, topography, health system issues, and identifies potential constraints and challenges to achieve the elimination stage, such as inter-districts coordination, decentralization policy and allocation of financial resources for the programme. Historical malaria data from 2007 to 2011 were collected through secondary data, in-depth interviews and focus group discussions during study year (2010–2011). Malaria cases were mapped using the village-centroid shape file to visualize its distribution with geomorphologic characteristics overlay and spatial distribution of malaria. API in each village in Purworejo and its surrounding districts from 2007 to 2011 was stratified into high, middle or low case incidence to show the spatiotemporal mapping pattern. The spatiotemporal pattern of malaria cases in Purworejo and the adjacent districts demonstrate repeated concentrated occurrences of malaria in specific areas from 2007 to 2011. District health system issues, i.e., suboptimal coordination between primary care and referral systems, suboptimal inter-district collaboration for malaria surveillance, decentralization policy and the lack of resources, especially district budget allocations for the malaria programme, were major constraints for programme sustainability. A new malaria elimination approach that fits the local disease transmission, intervention and political system is required. These changes include timely measurements of malaria transmission, revision of the decentralized government system and optimizing the use of the district capitation fund followed by an effective technical implementation of the intervention strategy.