الملخص
ABSTRACT Objective: This study aimed to present a temporal and spatial analysis of the 2018 measles outbreak in Brazil, particularly in the metropolitan city of Manaus in the Amazon region, and further introduce a new tool for spatial analysis. Methods: We analyzed the geographical data of the residences of over 7,000 individuals with measles in Manaus during 2018 and 2019. Spatial and temporal analyses were conducted to characterize various aspects of the outbreak, including the onset and prevalence of symptoms, demographics, and vaccination status. A visualization tool was also constructed to display the geographical and temporal distribution of the reported measles cases. Results: Approximately 95% of the included participants had not received vaccination within the past decade. Heterogeneity was observed across all facets of the outbreak, including variations in the incubation period and symptom presentation. Age distribution exhibited two peaks, occurring at one year and 18 years of age, and the potential implications of this distribution on predictive analysis were discussed. Additionally, spatial analysis revealed that areas with the highest case densities tended to have the lowest standard of living. Conclusion: Understanding the spatial and temporal spread of measles outbreaks provides insights for decision-making regarding measures to mitigate future epidemics.
الملخص
Infectious diseases significantly contribute to global morbidity and mortality, highlighting the critical need for robust disease surveillance systems. The rapid and accurate identification of infection hotspots is crucial for effective disease control and eliminating vector reservoirs. Traditional methods, reliant on patient-reported data, are vague, slow, and non-integrative, presenting substantial barriers to fully understanding the underlying causes of infection transmission. The widespread usage of smartphones presents a unique opportunity to access, analyze, and monitor digital data. Particularly, location data can offer potential insights into infectious disease dynamics, which has remained largely unexplored. Firstly, the present study leverages location history data from smartphones of malaria patients in Manaus, Amazonas region, to pinpoint mosquito-breeding sites. Upon quantifying the location data, the primary transmission hotspots were identified to be concentrated on the outskirts of the city of Manaus. Additionally, the quantification and hotspot validation confirmed that newly visited locations during the exposure period were potential sources of infection transmission. Secondly, the current study also employs a novel digital contact investigation method for a human-to-human transmission infection such as tuberculosis to measure the exposure risk between the active index cases and their close contacts. The digital contact investigation revealed varied exposure durations between the recruited paired index and close contact participants based on the outcome of close contact. To summarize, the present study determines distinct mobility patterns associated with both these infectious diseases, potentially aiding in drafting targeted public health strategies and policies for digital epidemiological surveillance
As doenças infecciosas são um dos principais contribuintes para a morbidade e a mortalidade globais, enfatizando a necessidade crítica de sistemas robustos de vigilância de doenças. A identificação rápida e precisa dos pontos críticos de infecção é fundamental para o controle eficaz de doenças e a eliminação de reservatórios de vetores. Os métodos tradicionais, que dependem de dados relatados por pacientes, são vagos, lentos e não integrativos, apresentando barreiras significativas para a compreensão total das causas subjacentes da transmissão de infecções. O uso generalizado de dispositivos móveis apresenta uma oportunidade única de acessar, analisar e monitorar dados digitais. Especialmente, dados de localização podem oferecer informações úteis sobre a dinâmica de doenças infecciosas, que permanecem em grande parte inexploradas. Primeiramente, o presente estudo utiliza dados de histórico de localização de smartphones de pacientes com malária em Manaus, na região do Amazonas, para identificar locais de reprodução de mosquitos. Ao quantificar os dados de localização, identificaram-se os principais pontos de transmissão concentrados nos arredores da cidade de Manaus. Além do mais, a quantificação e a validação em campo confirmaram que os locais recém-visitados durante o período de exposição eram potenciais fontes de transmissão da infecção. Em segundo lugar, o estudo atual também emprega um inovador método de investigação digital de contato para uma infecção por transmissão de humano para humano, como a tuberculose, a fim de medir o risco por exposição entre os casos índice ativos e seus contatos próximos. A investigação digital de contato revelou períodos de exposição variados entre os participantes recrutados em pares de casos índice e contatos próximos, com base no resultado do contato próximo. Em resumo, o presente estudo identifica padrões distintos de mobilidade associados a ambas essas doenças infecciosas, auxiliando potencialmente na elaboração de estratégias e políticas de saúde pública direcionadas para a vigilância epidemiológica digital
الموضوعات
Patients/classification , Communicable Diseases/classification , Cell Phone/instrumentation , Tuberculosis/pathology , Geographic Information Systems , Malaria/pathologyالملخص
The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/. OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.