Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study

Herbuela, Von Ralph Dane Marquez; Karita, Tomonori; Carvajal, Thaddeus M.; Ho, Howell Tsai; Lorena, John Michael Olea; Regalado, Rachele Arce; Sobrepeña, Girly Dirilo; Watanabe, Kozo

doi:10.2196/19034

Cited by 14 publications

(9 citation statements)

References 21 publications

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“…The governments of ASEAN countries should also prioritize digitizing their dengue surveillance systems. Mobile-based dengue surveillance systems have been developed for 2 cities in the Philippines and Indonesia and have shown promising results [ 24 , 25 ]. These systems allow healthcare workers to rapidly notify local and national healthcare authorities for every confirmed dengue case.…”

Section: Recommendationsmentioning

confidence: 99%

Dengue and COVID-19 infections in the ASEAN region: a concurrent outbreak of viral diseases

Wiyono¹,

Rocha

Cedeño

et al. 2021

Epidemiol Health

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The countries of the Association of Southeast Asian Nations (ASEAN) have been dealing with dengue outbreaks for decades, being one of the regions with high dengue infection cases globally. These outbreaks are happening on top of the COVID-19 pandemic that is causing havoc among these ten countries. Both infectious diseases have caused a tremendous burden for the countries in the region-from infection control to the economic impact. This increasing number of cases happened with the contribution of the disruption in the pathogen-host-vector relationship, caused by the change of human behaviour during the pandemic period, and aggravated by the implementation of lockdowns and social distancing policies. These had several consequences, including limiting the coverage of dengue preventive programs and delaying the medical management of both diseases due to co-infection and misdiagnosis. It is of utmost importance to maintain the awareness of the population of both diseases and to devise strategies on dengue vector control to properly address the infection with digitalization and remote surveillance.Similarly, critical triage algorithms and further research are also needed to address co-infection and misdiagnosis. Management of COVID-19 using vaccination also should be optimized to subside the pandemic.

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Section: Recommendationsmentioning

confidence: 99%

Dengue and COVID-19 infections in the ASEAN region: a concurrent outbreak of viral diseases

Wiyono¹,

Rocha

Cedeño

et al. 2021

Epidemiol Health

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“…Broad trends in public health and epidemiology informatics include an increased interest in surveillance methods based on mobile apps for early detection of disease outbreaks [8]. Public health issues related to the opioid drug epidemic [9], antimicrobial resistance [10], or the impact of environmental pollution [11] are more and more analysed on a wide scale using data sources such as social media, internet search data, or Wikitrends and on a variety of modalities including images, text and spatial data.…”

Section: Overall Observationmentioning

confidence: 99%

“…Wang et al, [5] Moncada-Torres et al, [6] Xiao et al, [7] Herbuela et al, [8] Homer and Wakeland [9] Kuzmenkov et al, [10] Razavi-Termeh et al,. [11] Zwan et al,.…”

Section: Authors and Referencesmentioning

confidence: 99%

Novelty in Public Health and Epidemiology Informatics

Diallo

Bordea

2022

Yearb Med Inform

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Objectives: To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI). Methods: Similar to last year’s edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section. Results: Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images. Conclusion: Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues

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“…Ongoing systematic surveillance is important to help detect early outbreaks and to evaluate the effectiveness of public health measures and programs [27,28]. In this study, we attempted to conduct flexible analysis of freely available travel data collected from the CAPSCA dashboard to identify certain patterns and early signals of the COVID-19 pandemic, which may help policymakers take appropriate action.…”

Section: Principal Findingsmentioning

confidence: 99%

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis

Chu¹,

Chan²,

Tsang³

et al. 2021

JMIR Public Health Surveill

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Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

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Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study

Cited by 14 publications

References 21 publications

Dengue and COVID-19 infections in the ASEAN region: a concurrent outbreak of viral diseases

Dengue and COVID-19 infections in the ASEAN region: a concurrent outbreak of viral diseases

Novelty in Public Health and Epidemiology Informatics

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis

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