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Preprint in English | medRxiv | ID: ppmedrxiv-22270483


Although COVID-19 vaccines are globally available, waning immunity and emerging vaccine-evasive variants of concern have hindered the international response as COVID-19 cases continue to rise. Mitigating COVID-19 requires testing to identify and isolate infectious individuals. We developed a stochastic compartmentalized model to simulate SARS-CoV-2 spread in the United States and India using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) assays, rapid antigen tests, and vaccinations. We detail the optimal testing frequency and coverage in the US and India to mitigate an emerging outbreak even in a vaccinated population: overall, maximizing frequency is more important, but high coverage remains necessary when there is sustained transmission. We show that a resource-limited vaccination strategy still requires high-frequency testing and is 16.50% more effective in India than the United States. Tailoring testing strategies to transmission settings can help effectively reduce cases more than if a uniform approach is employed without regard to differences in location.

Preprint in English | medRxiv | ID: ppmedrxiv-20153643


Although most COVID-19 cases have occurred in low-resource countries, there is scarce information on the epidemiology of the disease in such settings. Comprehensive SARS-CoV-2 testing and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh reveal stark contrasts from epidemics affecting high-income countries, with 92.1% of cases and 59.7% of deaths occurring among individuals <65 years old. The per-contact risk of infection is 9.0% (95% confidence interval: 7.5-10.5%) in the household and 2.6% (1.6-3.9%) in the community. Superspreading plays a prominent role in transmission, with 5.4% of cases accounting for 80% of infected contacts. The case-fatality ratio is 1.3% (1.0-1.6%), and median time-to-death is 5 days from testing. Primary data are urgently needed from low- and middle-income countries to guide locally-appropriate control measures.

Preprint in English | medRxiv | ID: ppmedrxiv-20132787


BackgroundThe rapid spread of COVID-19 globally has prompted policymakers to evaluate the capacity of health care infrastructure in their communities. Many hard-hit localities have witnessed a large influx of severe cases that strained existing hospitals. As COVID-19 spreads in India, it is essential to evaluate the countrys capacity to treat severe cases. MethodsWe combined data on public and private sector hospitals in India to produce state level estimates of hospital beds, ICU beds, and mechanical ventilators. Based on the number of public sector hospitals from the 2019 National Health Profile (NHP) of India and the relative proportions of public and private health care facilities from the National Sample Survey (NSS) 75th round (2017-2018), we estimated capacity in each Indian state and union territory (UT). We assumed that 5% of all hospital beds were ICU beds and that 50% of ICU beds were equipped with ventilators. ResultsWe estimated that India has approximately 1.9 million hospital beds, 95,000 ICU beds and 48,000 ventilators. Nationally, resources are concentrated in the private sector (hospital beds: 1,185,242 private vs 713,986 public; ICU beds: 59,262 private vs 35,699 public; ventilators: 29,631 private vs. 17,850 public). Our findings suggest substantial variation in available resources across states and UTs. ConclusionSome projections shave suggested a potential need for approximately 270,000 ICU beds in an optimistic scenario, over 2.8 times the estimated number of total available ICU beds in India. Additional resources will likely be required to accommodate patients with severe COVID-19 infections in India.

Preprint in English | medRxiv | ID: ppmedrxiv-20105239


ObjectiveThe novel coronavirus, COVID-19, has rapidly emerged to become a global pandemic and is known to cause a high risk to patients over the age of 70 and those with co-morbidities, such as hypertension and diabetes. Though children are at comparatively lower risk compared to adults, the Indian population has a large young demographic that is likely to be at higher risk due to exposure to pollution, malnutrition and poor access to medical care. We aimed to quantify the potential impact of COVID-19 on Indias child population. MethodsWe combined district family household survey data with data from the COVID-19 outbreak in China to analyze the potential impact of COVID-19 on children under the age of 5, under three different scenarios; each of which assumed the prevalence of infection to be 0.5%, 1%, or 5%. ResultsWe find that in the lowest prevalence scenario, across the most populous 18 Indian states, asymptomatic, non-hospitalized symptomatic and hospitalized symptomatic cases could reach 87,200, 412,900 and 31,900, respectively. In a moderate prevalence scenario, these figures reach 174,500, 825,800, and 63,800, and in the worst case, high prevalence scenario these cases could climb as high as 872,200, 4,128,900 and 319,700. ConclusionThese estimates show COVID-19 has the potential to pose a substantial threat to Indias large population of children, particularly those suffering from malnutrition and exposure to indoor air pollution, who may have limited access to health services.

Preprint in English | medRxiv | ID: ppmedrxiv-20079749


IntroductionDespite measures such as travel restrictions and lockdowns, the novel coronavirus (SARS-COV-2) is projected to spread across India. Considering that a vaccine for COVID-19 is will not be available soon, it is important to identify populations with high risk from COVID-19 and take measures to prevent outbreaks and build healthcare infrastructure at the local level. MethodsWe used data from two large nationally representative household surveys, administrative sources, and published studies to estimate the risk of COVID-19 at the district level in India. We employed principal component analysis to create an index of the health risk of COVID-19 from demographic and comorbidity indicators such as the proportions of elderly population and rates of diabetes, hypertension, and respiratory illnesses. Another principal component index examined the socioeconomic and healthcare access risk from COVID-19, based on the standard of living, proportion of caste groups, and per capita access to public healthcare in each district. ResultsDistricts in northern, southern and western Indian states such as Punjab, Tamil Nadu, Kerala, and Maharashtra were at the highest health risk from COVID-19. Many of these districts have been designated as COVID-19 hotspots by the Indian government because of emergent outbreaks. Districts in eastern and central states such as Uttar Pradesh, Bihar, and Madhya Pradesh have higher socioeconomic and healthcare access risk as compared with other areas. ConclusionDistricts at high risk of COVID-19 should prioritize policy measures for preventing outbreaks, and improving critical care infrastructure and socioeconomic safety nets.

Preprint in English | medRxiv | ID: ppmedrxiv-20051995


BackgroundCOVID-19 originated in China and has quickly spread worldwide causing a pandemic. Countries need rapid data on the prevalence of the virus in communities to enable rapid containment. However, the equipment, human and laboratory resources required for conducting individual RT-PCR is prohibitive. One technique to reduce the number of tests required is the pooling of samples for analysis by RT-PCR prior to testing. MethodsWe conducted a mathematical analysis of pooling strategies for infection rate classification using group testing and for the identification of individuals by testing pooled clusters of samples. FindingsOn the basis of the proposed pooled testing strategy we calculate the probability of false alarm, the probability of detection, and the average number of tests required as a function of the pool size. We find that when the sample size is 256, using a maximum pool size of 64, with only 7.3 tests on average, we can distinguish between prevalences of 1% and 5% with a probability of detection of 95% and probability of false alarm of 4%. InterpretationThe pooling of RT-PCR samples is a cost-effective technique for providing much-needed course-grained data on the prevalence of COVID-19. This is a powerful tool in providing countries with information that can facilitate a response to the pandemic that is evidence-based and saves the most lives possible with the resources available. FundingBill & Melinda Gates Foundation Authors contributionsRL and KRN conceived the study. IF, KT, KRN, SB and RL all contributed to the writing of the manuscript and AH and JJ provided comments. KRN and AH conducted the analysis and designed the figures. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe pooling of RT-PCR samples has been shown to be effective in screening for HIV, Chlamydia, Malaria, and influenza, among other pathogens in human health. In agriculture, this method has been used to assess the prevalence of many pathogens, including Dichelobacter nodosus, which causes footrot in sheep, postweaning multisystemic wasting syndrome, and antibiotic resistance in swine feces, in addition to the identification of coronaviruses in multiple bat species. In relation to the current pandemic, researchers in multiple countries have begun to employ this technique to investigate samples for COVID-19. Added value of this studyGiven recent interest in this topic, this study provides a mathematical analysis of infection rate classification using group testing and calculates the probability of false alarm, the probability of detection, and the average number of tests required as a function of the pool size. In addition the identification of individuals by pooled cluster testing is evaluated. Implications of all the available evidenceThis research suggests the pooling of RT-PCR samples for testing can provide a cheap and effective way of gathering much needed data on the prevalence of COVID-19 and identifying infected individuals in the community, where it may be infeasible to carry out a high number of tests. This will enable countries to use stretched resources in the most appropriate way possible, providing valuable data that can inform an evidence-based response to the pandemic.