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Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21253853


Low- and middle-income countries (LMICs) remain of high potential for hotspots for COVID-19 deaths and emerging variants given the inequality of vaccine distribution and their vulnerable healthcare systems. We aim to evaluate containment strategies that are sustainable and effective for LMICs. We constructed synthetic populations with varying contact and household structures to capture LMIC demographic characteristics that vary across communities. Using an agent- based model, we explored the optimal containment strategies for rural and urban communities by designing and simulating setting-specific strategies that deploy rapid diagnostic tests, symptom screening, contact tracing and physical distancing. In low-density rural communities, we found implementing either high quality (sensitivity > 50%) antigen rapid diagnostic tests or moderate physical distancing could contain the transmission. In urban communities, we demonstrated that both physical distancing and case finding are essential for containing COVID-19 (average infection rate < 10%). In high density communities that resemble slums and squatter settlements, physical distancing is less effective compared to rural and urban communities. Lastly, we demonstrated contact tracing is essential for effective containment. Our findings suggested that rapid diagnostic tests could be prioritised for control and monitor COVID-19 transmission and highlighted that contact survey data could guide strategy design to save resources for LMICs. An accompanying open source R package is available for simulating COVID-19 transmission based on contact network models.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20182469


BackgroundVirologic detection of SARS-CoV-2 through Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) has limitations for surveillance. Serologic tests can be an important complementary approach. ObjectiveAssess the practical performance of RT-PCR based surveillance protocols, and the extent of undetected SARS-CoV-2 transmission in Shenzhen, China. DesignCohort study nested in a public health response. SettingShenzhen, China; January-May 2020. Participants880 PCR-negative close-contacts of confirmed COVID-19 cases and 400 residents without known exposure (main analysis). Fifty-seven PCR-positive case contacts (timing analysis). MeasurementsVirological testing by RT-PCR. Measurement of anti-SARS-CoV-2 antibodies in PCR-negative contacts 2-15 weeks after initial testing using total Ab ELISA. Rates of undetected infection, performance of RT-PCR over the course of infection, and characteristics of seropositive but PCR-negative individuals were assessed. ResultsThe adjusted seropositivity rate for total Ab among 880 PCR-negative close-contacts was 4.1% (95%CI, 2.9% to 5.7%), significantly higher than among residents without known exposure to cases (0.0%, 95%CI, 0.0% to 1.0%). PCR-positive cases were 8.0 times (RR; 95% CI, 5.3 to 12.7) more likely to report symptoms than the PCR-negative individuals who were seropositive, but otherwise similar. RT-PCR missed 36% (95%CI, 28% to 44%) of infected close-contacts, and false negative rates appear to be highly dependent on stage of infection. LimitationsNo serological data were available on PCR-positive cases. Sample size was limited, and only 20% of PCR-negative contacts met inclusion criteria. ConclusionEven rigorous RT-PCR testing protocols may miss a significant proportion of infections, perhaps in part due to difficulties timing testing of asymptomatics for optimal sensitivity. Surveillance and control protocols relying on RT-PCR were, nevertheless, able to contain community spread in Shenzhen. Funding sourceBill & Melinda Gates Foundation, Special Foundation of Science and Technology Innovation Strategy of Guangdong Province of China, and Key Project of Shenzhen Science and Technology Innovation Commission, Shenzhen, China

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20141440


BackgroundCountries achieving control of COVID-19 after an initial outbreak will continue to face the risk of SARS-CoV-2 resurgence. This study explores surveillance strategies for COVID-19 containment based on polymerase chain reaction tests. MethodsUsing a dynamic SEIR-type model to simulate the initial dynamics of a COVID-19 introduction, we investigate COVID-19 surveillance strategies among healthcare workers, hospital patients, and community members. We estimate surveillance sensitivity as the probability of COVID-19 detection using a hypergeometric sampling process. We identify test allocation strategies that maximise the probability of COVID-19 detection across different testing capacities. We use Beijing, China as a case study. FindingsSurveillance subgroups are more sensitive in detecting COVID-19 transmission when they are defined by more COVID-19 specific symptoms. In this study, fever clinics have the highest surveillance sensitivity, followed by respiratory departments. With a daily testing rate of 0.07/1000 residents, via exclusively testing at fever clinic and respiratory departments, there would have been 598 [95% eCI: 35, 2154] and 1373 [95% eCI: 47, 5230] cases in the population by the time of first case detection, respectively. Outbreak detection can occur earlier by including non-syndromic subgroups, such as younger adults in the community, as more testing capacity becomes available. InterpretationA multi-layer approach that considers both the surveillance sensitivity and administrative constraints can help identify the optimal allocation of testing resources and thus inform COVID-19 surveillance strategies. FundingBill & Melinda Gates Foundation, National Institute of Health Research (UK), National Institute of Health (US), the Royal Society, and Wellcome Trust.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20042374


BackgroundThe Chinese government implemented a metropolitan-wide quarantine of Wuhan city on 23rd January 2020 to curb the epidemic of the coronavirus COVID-19. Lifting of this quarantine is imminent. We modelled the effects of two key health interventions on the epidemic when the quarantine is lifted. MethodWe constructed a compartmental dynamic model to forecast the trend of the COVID-19 epidemic at different quarantine lifting dates and investigated the impact of different rates of public contact and facial mask usage on the epidemic. ResultsWe estimated that at the end of the epidemic, a total of 65,572 (46,156-95,264) individuals would be infected by the virus, among which 16,144 (14,422-23,447, 24.6%) would be infected through public contacts, 45,795 (32,390-66,395, 69.7%) through household contact, 3,633 (2,344-5,865, 5.5%) through hospital contacts (including 783 (553-1,134) non-COVID-19 patients and 2,850 (1,801-4,981) medical staff members). A total of 3,262 (1,592-6,470) would die of COVID-19 related pneumonia in Wuhan. For an early lifting date (21st March), facial mask needed to be sustained at a relatively high rate ([≥]85%) if public contacts were to recover to 100% of the pre-quarantine level. In contrast, lifting the quarantine on 18th April allowed public person-to-person contact adjusted back to the pre-quarantine level with a substantially lower level of facial mask usage (75%). However, a low facial mask usage (<50%) combined with an increased public contact (>100%) would always lead a significant second outbreak in most quarantine lifting scenarios. Lifting the quarantine on 25th April would ensure a smooth decline of the epidemics regardless of the combinations of public contact rates and facial mask usage. ConclusionThe prevention of a second epidemic is viable after the metropolitan-wide quarantine is lifted but requires a sustaining high facial mask usage and a low public contact rate.

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