India’s Fight against the COVID-19 Pandemic: Lessons and the Way Forward

In early 2020, when the COVID-19 pandemic was indiscriminately spreading around the world, the seeming ability of India, the world’s second most populous country (with over 1.3 billion people), to contain the virus within its borders and keep COVID-19 infection and mortality rates low relative to population size was seen as miraculous. However, the miracle ended when ‘second-wave’ hit India in April 2021. On 1 May 2021, India became the first country in the world to record more than 400,000 coronavirus infections in a single day. This exponential rise in COVID-19 cases started on 28 April 2021 when India recorded 379,459 new COVID-19 cases and 3,647 deaths. This marked the eighth straight day of more than 300,000 cases a day—making India the second-highest COVID-19 case count in the world (over 20 million) with over 25 per cent of the global deaths from COVID. The following examines India’s fight against the pandemic, the failure to contain the second wave, the lessons learned and the way forward.

COVID-19 Contact Tracing: Ghana’s Efforts in the Application of Geospatial Technology in Minimizing the Impact of the Pandemic

COVID-19 has presented unusual challenges for individuals, governments and societies across the globe. Several non-medical and non-pharmaceutical interventions have demonstrated to be critical in addressing the resultant impacts. One notable tool among these interventions is the application of technology in identifying infected persons or individuals coming into contact with those infected. Policy think-tanks have invested in geospatial technology and information systems to help resolve contact tracing inefficiencies to curtail the fast spread of the disease. This study highlights the extent of the application of geospatial technology in COVID-19 contact tracing in Ghana. Here, it was demonstrated that majority of young adults that form the greater part of Ghana’s population have access to digital devices which serve as primary catalysts in facilitating effective and efficient contact tracing. Case count of the pandemic continues to surge sharply from one month to the other since the first recorded case on March 12, 2020. A huge number of cases were recorded in the southern part of the country, as against cases recorded in the north. Mobility patterns depicted the migration of more people from regions with a high number of case count to regions with lower counts. We recommend a holistic and proactive approach to the use of smart mobile devices and applications in enhancing contact tracing. Privacy and data protection laws must be prioritized and supported by effective legislative and policy frameworks that serve as the legal basis for the management of personal information.

A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data

Vaccine efficacy is often assessed by counting disease cases in a clinical trial. A new quantitative framework proposed here (“PoDBAY,” Probability of Disease Bayesian Analysis), estimates vaccine efficacy (and confidence interval) using immune response biomarker data collected shortly after vaccination. Given a biomarker associated with protection, PoDBAY describes the relationship between biomarker and probability of disease as a sigmoid probability of disease (“PoD”) curve. The PoDBAY framework is illustrated using clinical trial simulations and with data for influenza, zoster, and dengue virus vaccines. The simulations demonstrate that PoDBAY efficacy estimation (which integrates the PoD and biomarker data), can be accurate and more precise than the standard (case-count) estimation, contributing to more sensitive and specific decisions than threshold-based correlate of protection or case-count-based methods. For all three vaccine examples, the PoD fit indicates a substantial association between the biomarkers and protection, and efficacy estimated by PoDBAY from relatively little immunogenicity data is predictive of the standard estimate of efficacy, demonstrating how PoDBAY can provide early assessments of vaccine efficacy. Methods like PoDBAY can help accelerate and economize vaccine development using an immunological predictor of protection. For example, in the current effort against the COVID-19 pandemic it might provide information to help prioritize (rank) candidates both earlier in a trial and earlier in development.

422. Sustainability of Improvements to Hand Hygiene Performance Throughout the COVID-19 Pandemic

Background Hand hygiene (HH) is a standard infection prevention and control precaution to be applied in healthcare settings to prevent transmission of COVID-19. Many healthcare institutions observed significant improvements in HH performance during wave one of the COVID-19 pandemic but the sustainability of this change is unknown. Our aim was to evaluate long-term HH performance throughout subsequent waves of the pandemic across acute care hospitals in Ontario, Canada. Methods HH adherence was measured using a previously validated group electronic monitoring system which was installed on all alcohol handrub and sink soap dispensers inside and outside each patient room across 56 inpatient units (35 wards and 21 critical care units) spanning 13 acute care hospitals (6 university and 7 community teaching hospitals) from 1 November 2019 to 31 May 2021. Daily HH adherence was compared with daily COVID-19 case count across Ontario. During this period, weekly performance continued to be reported to units but unit-based quality improvement discussions were inconsistent due to the COVID-19 response. Results Figure 1 depicts daily aggregate HH adherence plotted against the new daily COVID-19 case count across Ontario. An elevation in HH adherence was seen prior to the start of the first wave, rising almost to 80% and then remained above 70% for the peak of wave one. During waves two and three, peak COVID-19 case counts were associated with a maximum HH adherence of 51%, only marginally above the pre-pandemic baseline. After the end of wave one (from 1 July 2020 to 31 May 2021) the median HH performance was only 49% (interquartile range 47%-50%). Figure 1. Hand hygiene adherence across 13 acute care hospitals in comparison to overall new daily COVID-19 cases in Ontario Conclusion Initial improvements in HH adherence preceding the start of the COVID-19 pandemic were not sustained, possibly due to increasing comfort and reduced anxiety associated with providing care to COVID-19 patients leading to a perception of reduced COVID-19 transmission risk. These findings highlight the need for HH monitoring to be tied to longitudinal unit-led quality improvement in order to achieve durable changes in practice. Disclosures Susy S. Hota, MSc MD FRCPC, Finch Therapeutics (Research Grant or Support) Susy S. Hota, MSc MD FRCPC, Finch Therapeutics (Individual(s) Involved: Self): Grant/Research Support

Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection time series, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100,000 for the states of Florida and Texas. As we continue our fight against COVID-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution, for any region under consideration.

Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts

The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.

SARS-CoV-2 Transmission Potential and Policy Changes in South Carolina, February 2020 - January 2021

Introduction: We aimed to examine how public health policies influenced the dynamics of COVID-19 time-varying reproductive number (Rt) in South Carolina from February 26, 2020 to January 1, 2021. Methods: COVID-19 case series (March 6, 2020 - January 10, 2021) were shifted by 9 days to approximate the infection date. We analyzed the effects of state and county policies on Rt using EpiEstim. We performed linear regression to evaluate if per-capita cumulative case count varies across counties with different population size. Results: Rt shifted from 2-3 in March to <1 during April and May. Rt rose over the summer and stayed between 1.4 and 0.7. The introduction of statewide mask mandates was associated with a decline in Rt (-15.3%; 95% CrI, -13.6%, -16.8%), and school re-opening, an increase by 12.3% (95% CrI, 10.1%, 14.4%). Less densely populated counties had higher attack rate (p<0.0001). Conclusion: The Rt dynamics over time indicated that public health interventions substantially slowed COVID-19 transmission in South Carolina, while their relaxation may have promoted further transmission. Policies encouraging people to stay home, such as closing non-essential businesses, were associated with Rt reduction, while policies that encouraged more movement, such as re-opening schools, were associated with Rt increase.

Utility of Facebook’s Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study (Preprint)

BACKGROUND The COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI). OBJECTIVE Building off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique. METHODS In September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county’s total population, percent rurality, and distance between each county pair. RESULTS We found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county’s total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26). CONCLUSIONS These results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices.

Symptomatology associated with the diffusion of the SARS-CoV-2 Lambda variant in Peru: An infodemiologic analysis

The SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) Lambda variant rapidly diffused across Peru following its identification in December 2020, and had now spread worldwide. In this study, we investigated infodemiologic trends in symptomatology associated with the Coronavirus Disease 2019 (COVID-19) following the spread of SARS-CoV-2 Lambda variant in Peru, enabling infodemiologic surveillance of SARS-CoV-2 in regions with high circulation of this new variant. Weekly Google Trends scores were obtained for key symptom keywords between March 1st, 2020 and July 4th, 2021, whilst case count data were obtained from Peruvian Ministry of Health. Multiple time series linear regression was used to assess trends in each score series, using the week of December 27th as cutoff for emergence of the Lambda variant. The significance of such trends was tested for each time period, before and after the cutoff date. A total 2,075,484 confirmed SARS-CoV-2 infections in Peru in relation to Google Trends data were analyzed. After Lambda variant emergence, searches for diarrhea demonstrated a change from a negative to positive correlation with weekly case counts and anticipated dynamic changes in case counts by 1-5 weeks. Searches for shortness of breath and headache remained consistently positively correlated to weekly case counts before and after Lambda emergence. No changes in searches for other common cold symptoms were observed, while no specific trends were observed for taste loss or smell loss. Diarrhea, headache, and shortness of breath appear to be the most important symptoms for infodemiologic tracking the current outbreak in Peru and other regions with high circulation of SARS-CoV-2 Lambda variant.