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Strategizing COVID-19 lockdowns using mobility patterns.

ABSTRACT: During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.

SUBMITTER: Buchel O

PROVIDER: S-EPMC8633798 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) was declared a pandemic in March 2020, triggering important changes for the entire society and healthcare systems, as well as significant lockdown measures aimed to limit the disease spread. We herein intended to catch the dynamic of Romanian physicians' perceptions of COVID-19 impact. For this purpose, after a literature review, a 30-item questionnaire was designed. The questionnaire was twice distributed online, about 1 month apart, during which partial relaxation measures were decreed in Romania. The questionnaire was voluntarily filled in by Romanian physicians who were willing to participate in the study. A total of 214 physicians answered the questionnaire upon its first release, and 199 respondents were registered upon its second release, most of whom (94.9%) were involved in clinical work, with one-third working in units dedicated to COVID-19 patients. In parallel with the relaxation of lockdown measures, along with increased confidence in the efficiency of protective measures (46.7% vs. 31.3%), separation from household members decreased from 36.9% to 22.1%. Nevertheless, the feeling of rejection felt by doctors remained similar (22.4% vs. 24.6%). Furthermore, answers regarding the clinical picture, diagnostic approach, and treatment options are discussed. Most of therapeutic options considered for SARS-CoV-2 treatment (e.g., lopinavir/ritonavir, oseltamivir, hydroxychloroquine, azithromycin, tocilizumab, and convalescent plasma) failed to confirm significant efficiency. On the contrary, vaccines for widescale use are already available despite the initial skepticism. In the beginning of the pandemic, 25.2% (18.2% vs. 32.2%) considered that there will not be an effective COVID-19 vaccine, while 41.6% (43.0% vs. 40.2%) thought that a vaccine would be available after at least 12 months. In conclusion, initially, following only a 1 month period, Romanian physicians' intention to consider treatments such as hydroxychloroquine or lopinavir/ritonavir for COVID-19 decreased significantly. Moreover, confidence in the efficiency of available protective measures increased, and the rates of separation from household members decreased.

Project description:BackgroundContact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be.MethodsWe analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted.ResultsModels using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998.ConclusionsUsing anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.

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Strategizing COVID-19 lockdowns using mobility patterns.

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