ABSTRACT
BackgroundDuring the COVID-19 outbreak, medical resources were primarily allocated to COVID-19, which might have reduced facility capacity for HIV testing. Further, people may have opted against HIV testing during this period to avoid COVID-19 exposure. We investigate the influence of the COVID-19 pandemic on HIV testing and its consequences in Japan. MethodsWe analysed quarterly HIV/AIDS-related data from 2015 to the second quarter of 2020 using an anomaly detection approach. The data included the number of consultations that public health centers received, the number of HIV tests performed by public health centers or municipalities, and the number of newly reported HIV cases with and without AIDS diagnosis. As sensitivity analyses, we performed the same analysis for two subgroups: men who have sex with men (MSM) and non-Japanese. FindingsThe number of HIV tests (9,584 vs. 35,908 in the year-before period) and consultations (11,689 vs. 32,565) performed by public health centers significantly declined in the second quarter of 2020, while the proportion of HIV cases with AIDS diagnosis among all HIV cases (36{middle dot}2% vs. 26{middle dot}4%) significantly increased after removing the trend and seasonality effects. The number of HIV cases without AIDS diagnosis numerically decreased (166 vs. 217), although the reduction was not significant. We confirmed similar trend for the MSM and non-Japanese groups. InterpretationThe current HIV testing system including public health centers misses more HIV cases at the early phase of the infection during the pandemic. Given that the clear epidemiological picture of HIV incidence during the pandemic is still uncertain, continuously monitoring the situation as well as securing sufficient test resources using self-test is essential. FundingJapan Society for the Promotion of Science, Japan Science and Technology Agency, Japan Agency for Medical Research and Development. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSBefore this study, we searched PubMed, Medline, and Google Scholar on Oct 12, 2020, for articles investigated the number of HIV test and HIV cases during the COVID-19 pandemic in Japan, using the search terms "novel coronavirus" or "SARS-CoV-2", and "HIV" or "AIDS", and "Japan", with no time restrictions. We found no published work relevant to our study. Added value of this studyDuring the COVID-19 pandemic in Japan, the public health centers and municipalities temporarily suspended facility-based HIV testing to concentrate their limited resources to COVID-19 testing. We investigated the impact of the COVID-19 pandemic on the number of HIV tests in public health centers and municipalities, and on the number of HIV cases with and without AIDS diagnosis. We confirmed that the number of the test declined in the second quarter (April to June) of 2020, and the proportion of HIV with AIDS diagnosis among all HIV cases increased during the same period. Implications of all the available evidenceProviding sufficient HIV testing opportunities even during the pandemic, when facility-based testing is challenging, is necessary for better clinical and public health outcomes. Self-testing and home specimen collection (e.g. dried blood spot or oral fluid test) could be a key to fill the gap between the need for HIV testing and the constraints related to the COVID-19 outbreak.
Subject(s)
COVID-19ABSTRACT
In June 2020, Arizona, U.S., emerged as one of the world’s worst coronavirus disease 2019 (COVID-19) spots after the stay-at-home order was lifted in the middle of May. With the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three Arizona regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.Funding: N.Y. was partially supported by Nishihara Cultural Foundation. H.W. was partially supported by the National Natural Science Foundation (#1737861)Declaration of Interest: None to declare
Subject(s)
COVID-19ABSTRACT
In June 2020, Arizona, U.S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May. However, with the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.
Subject(s)
COVID-19ABSTRACT
The outbreak of COVID-19 disrupts the life of many people in the world. The state of Arizona in the U.S. emerges as one of the country's newest COVID-19 hot spots. Accurate forecasting for COVID-19 cases will help governments to implement necessary measures and convince more people to take personal precautions to combat the virus. It is difficult to accurately predict the COVID-19 cases due to many human factors involved. This paper aims to provide a forecasting model for COVID-19 cases with the help of human activity data from the Google Community Mobility Reports. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with the COVID-19 data from the New York Times at the county level in the state of Arizona in the U.S. The proposed model describes the combined effects of transboundary spread among county clusters in Arizona and human actives on the transmission of COVID-19. The results show that the prediction accuracy of this model is well acceptable (above 94\%). Furthermore, we study the effectiveness of personal precautions such as wearing face masks and practicing social distancing on COVID-19 cases at the local level. The localized analytical results can be used to help to slow the spread of COVID-19 in Arizona. To the best of our knowledge, this work is the first attempt to apply PDE models on COVID-19 prediction with the Google Community Mobility Reports.