Optimal use of COVID19 Ag-RDT screening at border crossings to prevent community transmission: a modeling analysis

BackgroundCountries around the world have implemented restrictions on mobility, especially cross-border travel to reduce or prevent SARS-CoV-2 community transmission. Rapid antigen testing (Ag-RDT), with on-site administration and rapid turnaround time may provide a valuable screening measure to ease cross-border travel while minimizing risk of local transmission. To maximize impact, we developed an optimal Ag-RDT screening algorithm for cross-border entry. MethodsUsing a previously developed mathematical model, we determined the daily number of imported COVID-19 cases that would generate no more than a relative 1% increase in cases over one month for different effective reproductive numbers (Rt) of the recipient country. We then developed an algorithm- for differing levels of Rt, arrivals per day, mode of travel, and SARS-CoV-2 prevalence amongst travelers-to determine the minimum proportion of people that would need Ag-RDT testing at border crossings to ensure no greater than the relative 1% community spread increase. FindingsWhen daily international arrivals and/or COVID-19 prevalence amongst arrivals increases, the proportion of arrivals required to test using Ag-RDT increases. At very high numbers of international arrivals/COVID-19 prevalence, Ag-RDT testing is not sufficient to prevent increased community spread, especially for lower levels of Rt. In these cases, Ag-RDT screening would need to be supplemented with other measures to prevent an increase in community transmission. InterpretationAn efficient Ag-RDT algorithm for SARS-CoV-2 testing depends strongly on Rt, volume of travel, proportion of land and air arrivals, test sensitivity, and COVID-19 prevalence among travelers. FundingUSAID, Government of the Netherlands

Changes in HIV Treatment Differentiated Care Utilization During the COVID-19 Pandemic in Zambia

BackgroundDifferentiated service delivery (DSD) models aim to lessen the burden of HIV treatment on patients and providers in part by reducing requirements for facility visits and extending dispensing intervals. With the advent of the COVID-19 pandemic, minimizing patient contact with healthcare facilities and other patients, while maintaining treatment continuity and avoiding loss to care, has become more urgent, resulting in efforts to increase DSD uptake. We assessed the extent to which DSD coverage and antiretroviral treatment (ART) dispensing intervals have changed during the COVID-19 pandemic in Zambia. MethodsWe used patient data from Zambias electronic medical record system (SmartCare) for 737 health facilities, representing about 3/4 of all ART patients nationally, to compare the numbers and proportional distributions of patients enrolled in DSD models in the six months before and six months after the first case of COVID-19 was diagnosed in Zambia in March 2020. Segmented linear regression was used to determine whether the introduction of COVID-19 into Zambia further accelerated the increase in DSD scale-up. ResultsBetween September 2019 and August 2020, 181,317 patients aged 15+ (81,520 and 99,797 from September 1, 2019 to March 1, 2020 and from March 1 to August 31, 2020, respectively) enrolled in DSD models in Zambia. Overall participation in all DSD models increased over the study period, but uptake varied by model. The rate of acceleration increased in the second period for home ART delivery (152%), [≤]2-month fast-track (143%), and 3-month MMD (139%). There were significant decelerations in the increase in enrolment for 4-6-month fast-track (-28%) and other models (-19%). ConclusionsParticipation in DSD models for stable ART patients in Zambia increased after the advent of COVID-19, but dispensing intervals diminished. Eliminating obstacles to longer dispensing intervals, including those related to supply chain management, should be prioritized to achieve the expected benefits of DSD models and minimize COVID-19 risk.

Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using google search data in South Africa

Data from non-traditional data sources, such as social media, search engines, and remote sensing, have previously demonstrated utility for disease surveillance. Few studies, however, have focused on countries in Africa, particularly during the SARS-CoV-2 pandemic. In this study, we use searches of COVID-19 symptoms, questions, and at-home remedies submitted to Google to model COVID-19 in South Africa, and assess how well the Google search data forecast short-term COVID-19 trends. Our findings suggest that information seeking trends on COVID-19 could guide models for anticipating COVID-19 trends and coordinating appropriate response measures.

Previous and active tuberculosis in COVID-19 patients increases risk of death and prolongs recovery

BackgroundThere is growing literature on the association of SARS-CoV-2 and other chronic respiratory conditions, such as COPD and asthma. However, little is known about the relationship between coinfection with tuberculosis (TB) and COVID-19. We aimed to compare the risk and survival time of death and recovery among COVID-19 patients with and without TB. MethodsWe created a 4:1 propensity score matched sample of COVID-19 patients without and with TB, using SARS-CoV-2 surveillance data in the Philippines. We conducted a longitudinal cohort analysis of matched COVID-19 patients as of May 17, 2020, following them until June 15, 2020. The primary analysis estimated the risk ratios of death and recovery comparing COVID-19 patients with and without TB. Kaplan-Meier curves described time-to-death and time-to-recovery stratified by TB status, and differences in survival were assessed using the Wilcoxon test. We also conducted the same analysis on a subsample of admitted COVID-19 patients only. ResultsThe risk of death in COVID-19 patients with TB was 2.17 times greater compared to those without TB (95% CI: 1.40-3.37). The risk of recovery in TB patients was 25% less than the risk among those without TB (RR=0.75, 0.63-0.91). Similarly, time-to-death among COVID-19 patients with TB was significantly shorter (p=0.0031) and time-to-recovery in TB patients was significantly longer than patients without TB (p=0.0046). Among those admitted, COVID-19 TB patients also had a similar significant increase in risk of death (RR=2.25, 95% CI: 1.35-3.75); however, the risk of recovery was not significantly less (RR=0.84, 95% CI: 0.68-1.06). Time-to-death among those with TB was also significantly longer (p=0.0031) than those without TB, but there was no difference in time-to-recovery (p=0.17). ConclusionsOur findings show that coinfection with tuberculosis increases morbidity and mortality in COVID-19 patients. Our findings reiterate the need to prioritize routine and testing services for tuberculosis, even with increased disruptions to health systems during the SARS-CoV-2 pandemic. Additional research needs to focus on the interrelationship between TB and COVID-19 for appropriate planning and resource allocation, as SARS-CoV-2 continues to spread worldwide.

Epidemiological Profile and Transmission Dynamics of COVID-19 in the Philippines

The Philippines confirmed local transmission of COVID-19 on 7 March 2020. We described the characteristics and epidemiological time-to-event distributions for laboratory-confirmed cases in the Philippines. The median age of 8,212 cases was 46 years (IQR: 32-61), with 46.2% being female and 68.8% living in the National Capital Region. Health care workers represented 24.7% of all detected infections. Mean length of hospitalization for those who were discharged or died were 16.00 days (95% CI: 15.48, 16.54) and 7.27 days (95% CI: 6.59, 8.24). Mean duration of illness was 26.66 days (95% CI: 26.06, 27.28) and 12.61 days (95% CI: 11.88, 13.37) for those who recovered or died. Mean serial interval was 6.90 days (95% CI: 5.81, 8.41). Epidemic doubling time pre-quarantine (11 February and 19 March) was 4.86 days (95% CI: 4.67, 5.07) and the reproductive number was 2.41 (95% CI: 2.33, 2.48). During quarantine (March 20 to April 9), doubling time was 12.97 days (95% CI: 12.57, 13.39) and the reproductive number was 0.89 (95% CI: 0.78, 1.02).

Population density and basic reproductive number of COVID-19 across United States counties

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties, and whether population density could be used as a proxy for contact rates. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0. We also assessed whether this association was differential across county-level main mode of transportation-to-work percentage. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. The effect of population density and R0 was not modified by private transportation use. Differential R0 by population density can assist in more accurate predictions of the rate of spread of SARS-CoV-2 in areas that do not yet have active cases. Article Summary LineU.S. counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density.

Socioeconomic disparities in subway use and COVID-19 outcomes in New York City

BackgroundThe United States CDC has reported that racial and ethnic disparities in the COVID-19 pandemic may in part be due to socioeconomic disadvantages that require individuals to continue to work outside their home and a lack of paid sick leave.1 However, data-driven analyses of the socioeconomic determinants of COVID-19 burden are still needed. Using data from New York City (NYC), we aimed to determine how socioeconomic factors impact human mobility and COVID-19 burden. Methods/SummaryNew York City has a large amount of heterogeneity in socioeconomic status (SES) and demographics among neighborhoods. We used this heterogeneity to conduct a cross-sectional spatial analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. We also conducted a secondary analysis of NYC boroughs (which are equivalent to counties in the city) to assess the relationship between the decline in subway use and the time it took for each borough to end the exponential growth period of COVID-19 cases. FindingsAreas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare workers had more subway use during the pandemic. When adjusted for the percent of essential workers, these association do not remain; this suggests essential work is what drives subway use in lower SES zip codes and communities of color. Increased subway use was associated with a higher rate of COVID-19 cases per 100,000 population when adjusted for testing effort (aRR = 1.11; 95% CI: 1.03 - 1.19), but this association was weaker once we adjusted for median income (aRR = 1.06; 95% CI: 1.00 - 1.12). All sociodemographic variables were significantly associated with the rate of positive cases per 100,000 population when adjusting for testing effort (except percent uninsured) and adjusting for both income and testing effort. The risk factor with the strongest association with COVID-19 was the percent of individuals in essential work (aRR = 1.59, 95% CI: 1.36 - 1.86). We found that subway use declined prior to any executive order, and there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. InterpretationOur results suggest that the ability to stay home during the pandemic has been constrained by SES and work circumstances. Poorer neighborhoods are not afforded the same reductions in mobility as their richer counterparts. Furthermore, lower SES neighborhoods have higher disease burdens, which may be due to inequities in ability to shelter-in-place, and/or due to the plethora of other existing health disparities that increase vulnerability to COVID-19. Furthermore, the extended lag time between the dramatic fall in subway ridership and the end of the exponential growth phase for COVID-19 cases is important for future policy, because it demonstrates that if there is a resurgence, and stay-at-home orders are re-issued, then cities can expect to wait a month before reported cases will plateau.

Increased travel times to United States SARS-CoV-2 testing sites: a spatial modeling study

ImportanceAccess to testing is key to a successful response to the COVID-19 pandemic. ObjectiveTo determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time. DesignCross-sectional analysis of SARS-CoV-2 testing sites as of April 7, 2020 in relation to travel time. SettingUnited States COVID-19 pandemic. ParticipantsThe United States, including the 48 contiguous states and the District of Columbia. ExposuresPopulation density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data. Main OutcomeSARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites. Results6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time ({beta} = 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2x10-12), adjusting for population density. Conclusions and RelevanceGeographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may exacerbate health inequalities and bias county-specific transmission estimates. Geographic accessibility should be considered when planning the location of future testing sites and interpreting epidemiological data. Key PointsO_LISARS-CoV-2 testing sites are distributed unevenly in the US geography and population. C_LIO_LIMedian county-level travel time to SARS-CoV-2 testing sites is longer in less densely populated areas, and in areas with a higher percentage of minority or uninsured populations. C_LIO_LIImproved geographic accessibility to testing sites is imperative to manage the COVID-19 pandemic in the United States. C_LI