Dataset Information

Identifying geographic areas at risk of rubella epidemics in Japan using seroepidemiological data.

ABSTRACT:

Objective

Even with relatively high vaccination coverage, Japan experienced rubella epidemics in 2012-2014 and 2018-2019, which were fueled by untraced imported cases. We aimed to develop a risk map for rubella epidemics in Japan by geographic location via analysis of seroepidemiological data and accounting for the abundance of foreign visitors.

Methods

Geographic age distribution and seroprevalence were used to compute the age- and sex-dependent next-generation matrix in each region. We computed the probability of a major epidemic using the assumed number of untraced imported rubella cases proportionally modeled to the number of foreign travelers.

Results

Risks of a major epidemic were high in areas with capital cities, while areas with a greater fraction of older people yielded smaller effective reproduction numbers, a lower volume of foreign travelers, and thus a lower probability of a major epidemic. The volume of susceptible adult males was larger in urban geographic regions, having a greater number of foreign travelers than remote areas.

Conclusions

Our findings are consistent with the observation of multiple large clusters of rubella cases in urban areas during 2012-2014 and 2018-2019. Should a future rubella epidemic occur, it will likely be in geographic areas with capital cities.

SUBMITTER: Kayano T

PROVIDER: S-EPMC7526531 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying geographic areas at risk of rubella epidemics in Japan using seroepidemiological data.

Kayano Taishi T Lee Hyojung H Kinoshita Ryo R Nishiura Hiroshi H

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20200930

<h4>Objective</h4>Even with relatively high vaccination coverage, Japan experienced rubella epidemics in 2012-2014 and 2018-2019, which were fueled by untraced imported cases. We aimed to develop a risk map for rubella epidemics in Japan by geographic location via analysis of seroepidemiological data and accounting for the abundance of foreign visitors.<h4>Methods</h4>Geographic age distribution and seroprevalence were used to compute the age- and sex-dependent next-generation matrix in each reg ...[more]

PMID: 33010463

Similar Datasets

Project description:BACKGROUND: To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario. RESULTS: We observed over 80,000 ha of forest cover lost from 2000-2009 (0.21% annual loss), representing over 39 million Mg CO2. The rate increased rapidly following the enhancement of the Inter Oceanic Highway in 2005. Accessibility and distance to previous deforestation were strong predictors of deforestation risk, while land use designation was less important. The model performed consistently well (AUC?>?0.9), significantly better than random when we compared predicted deforestation risk to observed. If past deforestation rates continue, we estimate that 132,865 ha of forest could be lost by the year 2020, representing over 55 million Mg CO2. CONCLUSIONS: Maxent provided a reliable method for identifying areas at high risk of deforestation and the major explanatory variables that could draw attention for mitigation action planning under REDD+. The tool is accessible, replicable and easy to use; all necessary for producing good risk estimates and adapt models after potential landscape change. We propose this approach for developing countries planning to meet requirements under REDD+.

Project description:Despite ongoing control efforts, rabies remains an endemic zoonotic disease in many countries. Determining high-risk areas and the space-time patterns of rabies spread, as it relates to epidemiologically important factors, can support policymakers and program managers alike to develop evidence-based targeted surveillance and control programs. In this One Health approach which selected Thailand as the example site, the location-based risk of contracting dog-mediated rabies by both human and animal populations was quantified using a Bayesian spatial regression model. Specifically, a conditional autoregressive (CAR) Bayesian zero-inflated Poisson (ZIP) regression was fitted to the reported human and animal rabies case counts of each district, from the 2012-2017 period. The human population was used as an offset. The epidemiologically important factors hypothesized as risk modifiers and therefore tested as predictors included: number of dog bites/attacks, the population of dogs and cats, number of Buddhist temples, garbage dumps, animal vaccination, post-exposure prophylaxis, poverty, and shared administrative borders. Disparate sources of data were used to improve the estimated associations and predictions. Model performance was assessed using cross-validation. Results suggested that accounting for the association between human and animal rabies with number of dog bites/attacks, number of owned and un-owned dogs; shared country borders, number of Buddhist temples, poverty levels, and accounting for spatial dependence between districts, may help to predict the risk districts for dog-mediated rabies in Thailand. The fitted values of the spatial regression were mapped to illustrate the risk of dog-mediated rabies. The cross-validation indicated an adequate performance of the spatial regression model (AUC = 0.81), suggesting that had this spatial regression approach been used to identify districts at risk in 2015, the cases reported in 2016/17 would have been predicted with model sensitivity and specificity of 0.71 and 0.80, respectively. While active surveillance is ideal, this approach of using multiple data sources to improve risk estimation may inform current rabies surveillance and control efforts including determining rabies-free zones, and the roll-out of human post-exposure prophylaxis and anti-rabies vaccines for animals in determining high-risk areas.

Project description:BackgroundThe rubella vaccine was introduced into the immunization program in 1995 in the Shandong province, China. A series of different rubella vaccination strategies were implemented at different stages of measles control in Shandong province.Methodology/principal findingsThe average reported incidence rate of rubella cases remained at a low level in Shandong province after 1999. However, rubella epidemics occurred repeatedly in 2001/2002, 2006, and 2008/2009. The age of the onset of rubella cases gradually increased during 1999-2010, which showed that most cases were found among the 10 years old in 1999 and among the 17 years old in 2010. Phylogenetic analysis was performed and a phylogenetic tree was constructed based on the World Health Organization standard sequence window for rubella virus isolates. All rubella viruses isolated in Shandong province were divided into 4 genotypes: 1E, 1F, 2A, and 2B. Genotype 1E viruses accounted for the majority (79%) of all these viruses. The similarity of nucleotide and amino acid sequences among genotype 1E viruses was 98.2-100% and 99.1-100%, respectively. All Shandong genotype 1E strains, differed from international genotype 1E strains, belonged to cluster 1 and interdigitated with the viruses from other provinces in mainland China. The effective number of infections indicated by a bayesian skyline plot remained constant from 2001 to 2009.Conclusions/significanceThe gradual shift of disease burden to an older age group occurred after a rubella-containing vaccine was introduced into the childhood immunization schedule in 1995 in Shandong province. Four genotypes, including 1E, 1F, 2A, and 2B, were found in Shandong province during 2000-2009. Genotype 1E, rather than genotype 1F, became the predominant genotype circulating in Shandong province from 2001. All Shandong genotype 1E viruses belong to the genotype 1E/cluster 1; they have constantly circulated, and co-evolved and co-circulated, with those from other provinces.

Project description:BackgroundThe US Centers for Disease Control and Prevention (CDC) regularly issues "travel health notices" that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps.MethodsWe downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country.ResultsThe top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020.ConclusionWe created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation.

Dataset Information

Identifying geographic areas at risk of rubella epidemics in Japan using seroepidemiological data.

Objective

Methods

Results

Conclusions

Publications

Identifying geographic areas at risk of rubella epidemics in Japan using seroepidemiological data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets