RESUMEN
To dissect the transmission dynamics of SARS-CoV-2 in the United States, we integrate parallel streams of high-resolution data on contact, mobility, seasonality, vaccination and seroprevalence within a metapopulation network. We find the COVID-19 pandemic in the US is characterized by a geographically localized mosaic of transmission along an urban-rural gradient, with many outbreaks sustained by between-county transmission. We detect a dynamic tension between the spatial scale of public health interventions and population susceptibility as pre-pandemic contact is resumed. Further, we identify regions rendered particularly at risk from invasion by variants of concern due to spatial connectivity. These findings emphasize the public health importance of accounting for the hierarchy of spatial scales in transmission and the heterogeneous impacts of mobility on the landscape of contagion risk.
RESUMEN
A novel testing policy was implemented in May in France to systematically screen potential COVID-19 infections and suppress local outbreaks while lifting lockdown restrictions. 20,736 virologically-confirmed cases were reported in mainland France from May 13, 2020 (week 20, end of lockdown) to June 28 (week 26). Accounting for missing data and the delay from symptom onset to confirmation test, this corresponds to 7,258 [95% CI 7,160-7,336] cases with symptom onset during this period, a likely underestimation of the real number. Using age-stratified transmission models parameterized to behavioral data and calibrated to regional hospital admissions, we estimated that 69,115 [58,072-77,449] COVID-19 symptomatic cases occurred, suggesting that 9 out of 10 cases with symptoms were not ascertained. Median detection rate increased from 7% [6-9]% to 31% [28-35]% over time, with regional estimates varying from 11% (Grand Est) to 78% (Normandy) by the end of June. Healthcare-seeking behavior in COVID-19 suspect cases remained low (31%) throughout the period. Model projections for the incidence of symptomatic cases (4.5 [3.9-5.0] per 100,000) were compatible with estimates integrating participatory and virological surveillance data, assuming all suspect cases consulted. Encouraging healthcare-seeking behavior and awareness in suspect cases is critical to improve detection. Substantially more aggressive and efficient testing with easier access is required to act as a pandemic-fighting tool. These elements should be considered in light of the currently observed resurgence of cases in France and other European countries.
RESUMEN
On March 17, 2020, French authorities implemented a nationwide lockdown to respond to COVID-19 epidemic and curb the surge of patients requiring critical care, similarly to other countries. Evaluating the impact of lockdown on population mobility is essential to quantify achievable reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We used temporally resolved travel flows among 1,436 administrative areas of mainland France reconstructed from mobile phone trajectories. We measured mobility changes before and during lockdown at both local and country scales. Lockdown caused a 65% reduction in countrywide number of displacements, and was particularly effective in reducing work-related short-range mobility, especially during rush hours, and recreational trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localized in space. During lockdown, mobility drops were unevenly distributed across regions. They were strongly associated with active population, workers employed in sectors highly impacted by lockdown, and number of hospitalizations per region, and moderately associated with socio-economic level of the region. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting. Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, as anomalous mobility followed policy announcements that may act as seeding events. On the other hand, risk aversion may be beneficial in further decreasing mobility in largely affected regions. Socio-economic and demographic constraints to the efficacy of restrictions were also identified. The unveiled links between geography, demography, and timing of the response to mobility restrictions may help design interventions that minimize invasiveness while contributing to the current epidemic response. FundingANR projects EVALCOVID-19 (ANR-20-COVI-0007) and DATAREDUX (ANR-19-CE46-0008-03); EU H2020 grants RECOVER (H2020-101003589) and MOOD (H2020-874850); REACTing COVID-19 modeling grant.
RESUMEN
288 cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized importations timeline to assess the rapidity of isolation, and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the upcoming weeks. Time from travel to detection has considerably decreased since the first importation, however 6 cases out of 10 were estimated to go undetected. Countries outside China should be prepared for the possible emergence of several undetected clusters of chains of local transmissions.
RESUMEN
BackgroundThe novel coronavirus (2019-nCoV) epidemic has spread to 23 countries from China. Local cycles of transmission already occurred in 7 countries following case importation. No African country has reported cases yet. The management and control of 2019-nCoV introductions heavily relies on countrys health capacity. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of 2019-nCoV. MethodsWe used data on air travel volumes departing from airports in the infected provinces in China and directed to Africa to estimate the risk of introduction per country. We determined the countrys capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulation Monitoring and Evaluation Framework; and vulnerability, with the Infectious Disease Vulnerability Index. Countries were clustered according to the Chinese regions contributing the most to their risk. FindingsCountries at the highest importation risk (Egypt, Algeria, Republic of South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, Kenya) have variable capacity and high vulnerability. Three clusters of countries are identified that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and Beijing, respectively. InterpretationSeveral countries in Africa are stepping up their preparedness to detect and cope with 2019-nCoV importations. Resources and intensified surveillance and capacity capacity should be urgently prioritized towards countries at moderate risk that may be ill-prepared to face the importation and to limit onward transmission. FundingThis study was partially supported by the ANR project DATAREDUX (ANR-19-CE46-0008-03) to VC; the EU grant MOOD (H2020-874850) to MG, CP, MK, PYB, VC.
