Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 infection (PASC) in a Large Academic Medical Center in the US

ObjectiveA growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by Post-Acute Sequelae of SARS CoV-2 infection (PACS). Using electronic health records data, we aimed to characterize PASC-associated diagnoses and to develop risk prediction models. MethodsIn our cohort of 63,675 COVID-19 positive patients, 1,724 (2.7 %) had a recorded PASC diagnosis. We used a case control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into Phenotype Risk Scores (PheRSs) and evaluated their predictive performance. ResultsIn the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and 69 phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the COVID-19 positive cohort with an at least 2.9-fold increased risk for PASC. ConclusionsThe uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with a potential for risk stratification approaches. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/22281356v3_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@2ba229org.highwire.dtl.DTLVardef@a3651forg.highwire.dtl.DTLVardef@1440df1org.highwire.dtl.DTLVardef@ef7806_HPS_FORMAT_FIGEXP M_FIG C_FIG

A Case-Crossover Phenome-wide Association Study (PheWAS) for Understanding Post-COVID-19 Diagnosis Patterns

ImportancePost COVID-19 condition (PCC) is known to affect a large proportion of COVID-19 survivors. Robust study design and methods are needed to understand post-COVID-19 diagnosis patterns in all COVID-19 survivors, not just the ones clinically diagnosed with PCC. ObjectiveTo assess which diagnoses appear more frequently after a COVID-19 infection and how they differ by COVID-19 severity and vaccination status. DesignWe applied a case-crossover phenome-wide association study (PheWAS) in a retrospective cohort of COVID-19 survivors, comparing the occurrences of 1,649 diagnosis-based phenotype codes (PheCodes) pre- and post-COVID-19 infection periods in the same individual using a conditional logistic regression. SettingPatients tested for or diagnosed with COVID-19 at Michigan Medicine from March 10, 2020 through May 1, 2022. Participants36,856 SARS-CoV-2-positive patients and 141,615 age- and sex-matched SARS-CoV-2-negative patients as a comparison group for sensitivity analysis. ExposureSARS-CoV-2 virus infection as determined by RT-PCR testing and/or clinical evaluation. Main Outcomes and MeasuresWe compared the rate of occurrence of 1,649 disease classification codes in "pre-" and "post-COVID-19 periods". We studied how this pattern varied by COVID-19 severity and vaccination status at the time of infection. ResultsUsing a case-crossover PheWAS framework, we found mental, circulatory, and respiratory disorders to be strongly associated with the "post-COVID-19 period" for the overall COVID-19-positive cohort. A total of 325 PheCodes reached phenome-wide significance (p<3e-05), and top hits included cardiac dysrhythmias (OR=1.7 [95%CI: 1.6-1.9]), respiratory failure, insufficiency, arrest (OR=3.1 [95%CI: 2.7-3.5]) and anxiety disorder (OR=1.7 [95%CI: 1.6-1.8]). In the patients with severe disease, we found stronger associations with many respiratory and circulatory disorders, such as pneumonia (p=2.1e-18) and acute pulmonary heart disease (p=2.4e-8), and the "post-COVID-19 period," compared to those with mild/moderate disease. Test negative patients exhibited a somewhat similar association pattern to those fully vaccinated, with mental health and chronic circulatory diseases rising to the top of the association list in these groups. Conclusions and RelevanceOur results confirm that patients experience myriad symptoms more than 28 days after SARS-CoV-2 infection, but especially mental, circulatory, and respiratory disorders. Our case-crossover PheWAS approach controls for within-person confounders that are time-invariant. Comparison to test negatives with a similar design helped identify enrichment specific to COVID-19. As we look into the future, we must be aware of COVID-19 survivors healthcare needs in the period after infection. Key PointsO_ST_ABSQuestionC_ST_ABSWhat patterns of clinical diagnosis tend to occur more frequently after a COVID-19 infection and how do they vary by COVID-19 severity and vaccination status? FindingsIn a cohort of 36,856 COVID-19-positive patients, using a case-crossover phenome-wide association analysis that controls for within-subject confounders, we found symptoms such as anxiety disorder, cardiac dysrhythmias, and respiratory failure to be significantly associated with the "post-COVID-19 period." Patients with severe COVID-19 were more likely to receive diagnoses related to respiratory conditions in their "post-COVID-19 period" compared to those with mild/moderate COVID-19. The landscape of phenome-wide association signals for the vaccinated group featured common chronic conditions when compared to the signals in the unvaccinated group. MeaningSymptoms across multiple organ systems, especially in the mental, circulatory, and respiratory domains, were associated with the "post-COVID-19 period." Characterization of post-COVID-19 diagnosis patterns is crucial to understand the long term and future healthcare burden of COVID-19.

COVID-19 outcomes by cancer status, type, treatment, and vaccination

BackgroundObservational studies have identified patients with cancer as a potential subgroup of individuals at elevated risk of severe SARS-CoV-2 (COVID-19) disease and mortality. Early studies showed an increased risk of COVID-19 mortality for cancer patients, but it is not well understood how this association varies by cancer site, cancer treatment, and vaccination status. MethodsUsing electronic health record data from an academic medical center, we identified 259,893 individuals who were tested for or diagnosed with COVID-19 from March 10, 2020, to February 2, 2022. Of these, 41,218 tested positive for COVID-19 of whom 10,266 had a past or current cancer diagnosis. We conducted Firth-corrected, covariate-adjusted logistic regression to assess the association of cancer status, cancer type, and cancer treatment with four COVID-19 outcomes: hospitalization, intensive care unit (ICU) admission, mortality, and a composite "severe COVID-19" outcome which is the union of the first three outcomes. We examine the effect of the timing of cancer diagnosis and treatment relative to COVID diagnosis, and the effect of vaccination. ResultsCancer status was associated with higher rates of severe COVID-19 infection [OR (95% CI): 1.18 (1.08, 1.29)], hospitalization [OR (95% CI): 1.18 (1.06, 1.28)], and mortality [OR (95% CI): 1.22 (1.00, 1.48)]. These associations were driven by patients whose most recent initial cancer diagnosis was within the past three years. Chemotherapy receipt was positively associated with all four COVID-19 outcomes (e.g., severe COVID [OR (95% CI): 1.96 (1.73, 2.22)], while receipt of either radiation or surgery alone were not associated with worse COVID-19 outcomes. Among cancer types, hematologic malignancies [OR (95% CI): 1.62 (1.39, 1.88)] and lung cancer [OR (95% CI): 1.81 (1.34, 2.43)] were significantly associated with higher odds of hospitalization. Hematologic malignancies were associated with ICU admission [OR (95% CI): 1.49 (1.11, 1.97)] and mortality [OR (95% CI): 1.57 (1.15, 2.11)], while melanoma and breast cancer were not associated with worse COVID-19 outcomes. Vaccinations were found to reduce the frequency of occurrence for the four COVID-19 outcomes across cancer status but those with cancer continued to have elevated risk of severe COVID [cancer OR (95% CI) among those fully vaccinated: 1.69 (1.10, 2.62)] relative to those without cancer even among vaccinated. ConclusionOur study provides insight to the relationship between cancer diagnosis, treatment, cancer type, vaccination, and COVID-19 outcomes. Our results indicate that it is plausible that specific diagnoses (e.g., hematologic malignancies, lung cancer) and treatments (e.g., chemotherapy) are associated with worse COVID-19 outcomes. Vaccines significantly reduce the risk of severe COVID-19 outcomes in individuals with cancer and those without, but cancer patients are still at higher risk of breakthrough infections and more severe COVID outcomes even after vaccination. These findings provide actionable insights for risk identification and targeted treatment and prevention strategies.

Estimating COVID-19 Vaccination Effectiveness Using Electronic Health Records of an Academic Medical Center in Michigan

ImportanceSystematic characterization of the protective effect of vaccinations across time and at-risk populations is needed to inform public health guidelines and personalized interventions. ObjectiveTo evaluate the vaccine effectiveness (VE) over time and determine differences across demographic and clinical risk factors of COVID-19. Design, Setting, and ParticipantsThis test negative design consisted of adult patients who were tested or diagnosed for COVID-19 at Michigan Medicine in 2021. Variables extracted from Electronic Health Records included vaccination status, age, gender, race/ethnicity, comorbidities, body mass index, residential-level socioeconomic characteristics, past COVID-19 infection, being immunosuppressed, and health care worker status. ExposureThe primary exposure was vaccination status and was categorized into fully vaccinated with and without booster, partially vaccinated, or unvaccinated. Main Outcomes and MeasuresThe main outcomes were infection with COVID-19 (positive test or diagnosis) and having severe COVID-19, i.e., either being hospitalized or deceased. Based on these, VE was calculated by quarter, vaccine, and patient characteristics. ResultsOf 170,487 COVID-19 positive adult patients, 78,002 (45.8%) were unvaccinated, and 92,485 (54.2%) were vaccinated, among which 74,060 (80.1%) were fully vaccinated. COVID-19 positivity and severity rates were substantially higher among unvaccinated (12.1% and 1.4%, respectively) compared to fully vaccinated individuals (4.7% and 0.4%, respectively). Among 7,187 individuals with a booster, only 18 (0.3%) had a severe outcome. The covariate-adjusted VE against an infection was 62.1% (95%CI 60.3-63.8%), being highest in the Q2 of 2021 (90.9% [89.5-92.1%]), lowest in Q3 (60.1% [55.9-64.0%]), and rebounding in Q4 to 68.8% [66.3- 71.1%]). Similarly, VE against severe COVID-19 overall was 73.7% (69.6-77.3%) and remained high throughout 2021: 87.4% (58.1-96.3%), 92.2% (88.3-94.8%), 74.4% (64.8-81.5%) and 83.0% (78.8-86.4%), respectively. Data on fully vaccinated individuals from Q4 indicated additional protection against infection with an additional booster dose (VE-Susceptibility: 64.0% [61.1-66.7%] vs. 87.3% [85.0-89.2%]) and severe outcomes (VE-Severity: 78.8% [73.5-83.0%] vs. 94.0% [89.5-96.6%]). Comparisons between Pfizer-BioNTech and Moderna vaccines indicated similar protection against susceptibility (82.9% [80.7-84.9%] versus 88.1% [85.5- 90.2%]) and severity (87.1% [80.3-91.6%]) vs. (84.9% [76.2-90.5%]) after controlling for vaccination timing and other factors. There was no significant effect modification by all the factors we examined. Conclusions and RelevanceOur findings suggest that COVID-19 vaccines offered high protection against infection and severe COVID-19, and showed decreasing effectiveness over time and improved protection with a booster. Key PointsO_ST_ABSQuestionC_ST_ABSHow do the rates of COVID-19 outcomes (infections or mild/severe disease) compare across vaccination status and quarters of 2021, after adjusting for confounders? FindingsIn this cohort of 170,487 adult patients tested for or diagnosed with COVID-19 during 2021, both COVID-19 positivity and severity rates were substantially higher in unvaccinated compared to fully vaccinated individuals. Vaccine effectiveness estimation was adjusted for covariates potentially related to both being vaccinated and COVID-19 outcomes; this also allowed us to determine if effectiveness differed across patient subgroups. The estimated vaccine effectiveness across the four quarters of 2021 was 62.1% against infection and was 73.7% against severe COVID-19 (defined as hospitalization, ICU admission, or death). There was no significant effect modification by all the factors we examined. MeaningThese findings suggest COVID-19 vaccines had relatively high protection against infection and severe COVID-19 during 2021 for those who received two doses of an mRNA vaccine (Moderna or Pfizer-BioNTech) or one dose of the Janssen vaccine, of which the effectiveness decreased over time and improved with a booster.

Global Prevalence of Post-Acute Sequelae of COVID-19 (PASC) or Long COVID: A Meta-Analysis and Systematic Review

ImportanceAs SARS-CoV-2 pervades worldwide, considerable focus has been placed on the longer lasting health effects of the virus on the human host and on the anticipated healthcare needs. ObjectiveThe primary aim of this study is to examine the prevalence of post-acute sequelae of COVID-19 (PASC), commonly known as long COVID, across the world and to assess geographic heterogeneities through a systematic review and meta-analysis. A second aim is to provide prevalence estimates for individual symptoms that have been commonly reported as PASC, based on the existing literature. Data SourcesPubMed, Embase, and iSearch for preprints from medRxiv, bioRxiv, SSRN, and others, were searched on July 5, 2021 with verification extending to August 12, 2021. Study SelectionStudies written in English that consider PASC (indexed as ailments persisting at least 28 days after diagnosis or recovery for SARS-CoV-2 infection) and that examine corresponding prevalence, risk factors, duration, or associated symptoms were included. A total of 40 studies were included with 9 from North America, 1 from South America, 17 from Europe, 11 from Asia, and 2 from other regions. Data Extraction and SynthesisData extraction was performed and separately cross-validated on the following data elements: title, journal, authors, date of publication, outcomes, and characteristics related to the study sample and study design. Using a random effects framework for meta-analysis with DerSimonian-Laird pooled inverse-variance weighted estimator, we provide an interval estimate of PASC prevalence, globally, and across regions. This meta-analysis considers variation in PASC prevalence by hospitalization status during the acute phase of infection, duration of symptoms, and specific symptom categories. Main Outcomes and MeasuresPrevalence of PASC worldwide and stratified by regions. ResultsGlobal estimated pooled PASC prevalence derived from the estimates presented in 29 studies was 0.43 (95% confidence interval [CI]: 0.35, 0.63), with a higher pooled PASC prevalence estimate of 0.57 (95% CI: 0.45, 0.68), among those hospitalized during the acute phase of infection. Females were estimated to have higher pooled PASC prevalence than males (0.49 [95% CI: 0.35, 0.63] versus 0.37 [95% CI: 0.24, 0.51], respectively). Regional pooled PASC prevalence estimates in descending order were 0.49 (95% CI: 0.21, 0.42) for Asia, 0.44 (95% CI: 0.30, 0.59) for Europe, and 0.30 (95% CI: 0.32, 0.66) for North America. Global pooled PASC prevalence for 30, 60, 90, and 120 days after index test positive date were estimated to be 0.36 (95% CI: 0.25, 0.48), 0.24 (95% CI: 0.13, 0.39), 0.29 (95% CI: 0.12, 0.57) and 0.51 (95% CI: 0.42, 0.59), respectively. Among commonly reported PASC symptoms, fatigue and dyspnea were reported most frequently, with a prevalence of 0.23 (95% CI: 0.13, 0.38) and 0.13 (95% CI: 0.09, 0.19), respectively. Conclusions and RelevanceThe findings of this meta-analysis suggest that, worldwide, PASC comprises a significant fraction (0.43 [95% CI: 0.35, 0.63]) of COVID-19 tested positive cases and more than half of hospitalized COVID-19 cases, based on available literature as of August 12, 2021. Geographic differences appear to exist, as lowest to highest PASC prevalence is observed for North America (0.30 [95% CI: 0.32, 0.66]) to Asia (0.49 [95% CI: 0.21, 0.42]). The case-mix across studies, in terms of COVID-19 severity during the acute phase of infection and variation in the clinical definition of PASC, may explain some of these differences. Nonetheless, the health effects of COVID-19 appear to be prolonged and can exert marked stress on the healthcare system, with 237M reported COVID-19 cases worldwide as of October 12, 2021. Key Points QuestionAmong those infected with COVID-19, what is the global and regional prevalence of post-acute sequelae COVID-19 (PASC)? FindingsGlobally, the pooled PASC prevalence estimate was 0.43, whereas the pooled PASC prevalence estimate for patients who had to be hospitalized due to COVID-19 was 0.57. Regionally, estimated pooled PASC prevalence from largest to smallest effect size were 0.49 for Asia, 0.44 for Europe, and 0.30 for North America. Global pooled PASC prevalence for 30, 60, 90, and 120 days after index date were estimated to be 0.36, 0.24, 0.29, and 0.51, respectively. Among commonly reported PASC symptoms, fatigue and dyspnea were reported most frequently, with a prevalence of 0.23 and 0.13. MeaningIn follow-up studies of patients with COVID-19 infections, PASC was common both globally and across geographic regions, with studies from Asia reporting the highest prevalence.

SARS-CoV-2 infection fatality rates in India: systematic review, meta-analysis and model-based estimation

IntroductionFervorous investigation and dialogue surrounding the true number of SARS-CoV-2 related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nations devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India, through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from April 1, 2020 to June 30, 2021. MethodsFollowing PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch), were searched on July 3, 2021 (with results verified through August 15, 2021). Altogether using a two-step approach, 4,765 initial citations were screened resulting in 37 citations included in the narrative review and 19 studies with 41 datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analyze IFR1 which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provide lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2 related IFRs in India. We also try to stratify our empirical results across the first and the second wave. In tandem, we present updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from April 1, 2020 to June 30, 2021. ResultsFor India countrywide, underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3-29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4-11.9 with cumulative excess deaths ranging from 1.79-4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067 - 0.140) and 0.365% (95% CI: 0.264 - 0.504) to 0.485% (95% CI: 0.344 - 0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, the IFR1 generally appear to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290-1.316) to (0.241-0.651) %). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097 - 0.116) and 0.367% (95% CI: 0.358 - 0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data with the disadvantages being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. ConclusionWhen incorporating case and death underreporting, the meta-analyzed cumulative infection fatality rate in India varies from 0.36%-0.48%, with a case underreporting factor ranging from 25-30 and a death underreporting factor ranging from 4-12. This implies, by June 30, 2021, India may have seen nearly 900 million infections and 1.7-4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (covid19india.org) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India.

Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions

India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness. One Sentence SummaryAltered epidemiological characteristics of B.1.617.2 and delayed public health interventions contributed to the resurgence of SARS-CoV-2 in India from February to May 2021.

Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India

ObjectiveThere has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020 - January 31, 2021) and part of the second wave (February 1, 2021 - May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations. ResultsBoth wave 1 and wave 2 estimates qualitatively show that there is a large degree of "covert infections" in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71-11.47) and for deaths as 3.56 (95% CrI 3.48 - 3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26 - 28.81) and to 5.77 (95% CrI 5.34 - 6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates.

Changes in COVID-19-related outcomes and the impacts of the potential risk factors over time: a follow-up analysis

ImportanceCharacteristics of COVID-19 patients changed over the course of the pandemic. Understanding how risk factors changed over time can enhance the coordination of healthcare resources and protect the vulnerable. ObjectiveTo investigate the overall trend of severe COVID-19-related outcomes over time since the start of the pandemic, and to evaluate whether the impacts of potential risk factors, such as race/ethnic groups, changed over time. DesignThis retrospective cohort study included patients tested or treated for COVID-19 at Michigan Medicine (MM) from March 10, 2020, to September 2, 2020. According to the quarter in which they first tested positive, the COVID-19-positive cohort were stratified into three groups: Q1, March 1, 2020 - March 31, 2020; Q2, April 1, 2020 - June 30, 2020; Q3, July 1, 2020 - September 2, 2020. SettingsLarge, academic medical center. ParticipantsIndividuals tested or treated for COVID-19. ExposureExamined potential risk factors included age, race/ethnicity, smoking status, alcohol consumption, comorbidities, body mass index (BMI), and residential-level socioeconomic characteristics. Main Outcomes and MeasuresThe main outcomes included COVID-19-related hospitalization, intensive care unit (ICU) admission, and mortality, which were identified from the electronic health records from MM. ResultsThe study cohort consisted of 53,853 patients tested or treated for COVID-19 at MM, with mean (SD) age of 44.8 (23.1), mean (SD) BMI of 29.1 (7.6), and 23,814 (44.2%) males. Among the 2,582 patients who tested positive, 719 (27.8%) were hospitalized, 377 (14.6%) were admitted to ICU, and 129 (5.0%) died. The overall COVID-positive hospitalization rate decreased from 41.5% in Q1 to 12.6% in Q3, and the overall ICU admission rate decreased from 24.5% to 5.3%. Black patients had significantly higher (unadjusted) overall hospitalization rate (265 [41.1%] vs 326 [23.2%]), ICU admission rate (139 [21.6%] vs 172 [12.2%]), and mortality rate (42 [6.5%] vs 56 [4.0%]) than White patients. Each quarter, the hospitalization rate remained higher for Black patients compared to White patients, but this difference was attenuated over time for the (unadjusted) odds ratios (Q1: OR=1.9, 95% CI [1.25, 2.90]; Q2: OR=1.42, 95% CI [1.02, 1.98]; Q3: OR=1.36, 95% CI [0.67, 2.65]). Similar decreasing patterns were observed for ICU admission and mortality. Adjusting for age, sex, socioeconomic status, and comorbidity score, the racial disparities in hospitalization between White and Black patients were not significant in each quarter of the year (Q1: OR=1.43, 95% CI [0.75, 2.71]; Q2: OR=1.25, 95% CI [0.79, 1.98]; Q3: OR=1.76 95% CI [0.81, 3.85]), in contrast to what was observed in the full cohort (OR=1.85, 95% CI [1.39, 2.47]). Additionally, significant association of hospitalization with living in densely populated area was identified in the first quarter (OR= 664, 95% CI [20.4, 21600]), but such association disappeared in the second and third quarters (Q2: OR= 1.72 95% CI [0.22, 13.5]; Q3: OR=3.69, 95% CI [0.103, 132]). Underlying liver diseases were positively associated with hospitalization in White patients (OR=1.60, 95% CI [1.01, 2.55], P=.046), but not in Black patients (OR=0.49, 95% CI [0.23, 1.06], P=.072, Pint=.013). Similar results were obtained for the effect of liver diseases on ICU admission in White and Black patients (White: OR=1.75, 95% CI [1.01, 3.05], P=.047; Black: OR=0.46, 95% CI [0.17, 1.26], P=.130, Pint=.030). Conclusions and RelevanceThese findings suggest that the COVID-19-related hospitalization, ICU admission, and mortality rates were decreasing over the course of the pandemic. Although racial disparities persisted, the magnitude of the differences in hospitalization and ICU admission rates diminished over time. Key PointsO_ST_ABSQuestionsC_ST_ABSHow did the overall hospitalization and intensive care unit (ICU) admission rates change over the course of the pandemic and how did they vary by race? FindingsIn this cohort study of 2,582 patients testing positive for COVID-19, the unadjusted hospitalization rate decreased from 50.5% in Q1 (March 10, 2020, to March 31, 2020) to 17.9% in Q3 (July 1, 2020, to September 2, 2020) for Black patients, and from 23.2% in Q1 to 13.8% in Q3 for White patients. After adjusting for age, sex, sociodemographic factors, and comorbidity conditions, the odds ratios of hospitalization between White and Black patients were not significant in each quarter of the year 2020. No significant associations between ICU admission and race/ethnic groups were identified in each quarter or the entire three quarters. MeaningThese findings suggests an appreciable decline in hospitalization and ICU admission rates among COVID-19 positive patients. The hospitalization and ICU admission rates across race/ethnic groups became closer over time.

Optimal test allocation strategy for COVID-19

Testing for active SARS-CoV-2 infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies in the presence of limited resources. Inthis paper, we provide a mathematical framework for defining anoptimal strategy for allocating viral tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infected cases, but can also be used for long-time surveillance to monitor for new outbreaks, which will be especially important during ongoing vaccine distribution across the world. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of tests with different costs and accuracy (for example, the expensive but more accurate RT-PCR test versus the cheap but less accurate rapid antigen test), administered under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting to different stages of an epidemic, and it has broader relevance beyond the COVID-19 outbreak.

COVID-19 prediction in South Africa: Understanding the unascertained cases -- the hidden part of the epidemiological iceberg

Understanding the impact of non-pharmaceutical interventions as well as acscounting for the unascertained cases remain critical challenges for epidemiological models for understanding the transmission dynamics of COVID-19 spread. In this paper, we propose a new epidemiological model (eSEIRD) that extends the widely used epidemiological models such as extended Susceptible-Infected-Removed model (eSIR) and SAPHIRE (initially developed and used for analyzing data from Wuhan). We fit these models to the daily ascertained infected (and removed) cases from March 15, 2020 to Dec 31, 2020 in South Africa that reported the largest number of confirmed COVID-19 cases and deaths from the WHO African region. Using the eSEIRD model, the COVID-19 transmission dynamics in South Africa was characterized by the estimated basic reproduction number (R0) starting at 3.22 (95%CrI: [3.19, 3.23]) then dropping below 2 following a mandatory lockdown implementation and subsequently increasing to 3.27 (95%CrI: [3.27, 3.27]) by the end of 2020. The initial decrease of effective reproduction number followed by an increase suggest the effectiveness of early interventions and the combined effect of relaxing strict interventions and emergence of a new coronavirus variant in South Africa. The low estimated ascertainment rate was found to vary from 1.65% to 9.17% across models and time periods. The overall infection fatality ratio (IFR) was estimated as 0.06% (95%CrI: [0.04%, 0.22%]) accounting for unascertained cases and deaths while the reported case fatality ratio was 2.88% (95% CrI: [2.45%, 6.01%]). The models predict that from December 31, 2020, to April 1, 2021, the predicted cumulative number of infected would reach roughly 70% of total population in South Africa. Besides providing insights on the COVID-19 dynamics in South Africa, we develop powerful forecasting tools that enable estimation of ascertainment rates and IFR while quantifying the effect of intervention measures on COVID-19 spread. AMS ClassificationPlace Classification here. Leave as is, if there is no classification

EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy

The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number under such data anomalies using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.

A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Using COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For active case counts, SMAPE values are 0.72 (SEIR-fansy) and 33.83 (eSIR). For cumulative case counts, SMAPE values are 1.76 (baseline) 23.10 (eSIR), 2.07 (SAPHIRE) and 3.20 (SEIR-fansy). For cumulative death counts, the SMAPE values are 7.13 (SEIR-fansy) and 26.30 (eSIR). For cumulative cases and deaths, we compute Pearsons and Lins correlation coefficients to investigate how well the projected and observed reported COVID-counts agree. Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors as of June 30 and note that the SEIR-fansy model reports the highest underreporting factor for active cases (6.10) and cumulative deaths (3.62), while the SAPHIRE model reports the highest underreporting factor for cumulative cases (27.79).

Exposure and risk factors for COVID-19 and the impact of staying home on Michigan residents

COVID-19 has had a substantial impact on clinical care and lifestyles globally. The State of Michigan reports over 80,000 positive COVID-19 tests between March 1, 2020 and July 29, 2020. We surveyed 8,047 Michigan Medicine biorepository participants in late June 2020. We found that 58% of COVID-19 cases reported no known exposure to family members or to someone outside the house diagnosed with COVID-19. A significantly higher rate of COVID-19 cases were employed as essential workers (45% vs 19%, p=3x10-11). COVID-19 cases reporting a fever were more likely to require hospitalization (categorized as severe; OR = 4.6 [95% CI: 1.7-13.0, p=0.004]) whereas respondents reporting rhinorrhea was less likely to require hospitalization (categorized as mild-to-moderate; OR = 0.16 [95% CI: 0.04-0.70, p=0.016]). African-Americans reported higher rates of being diagnosed with COVID-19 (OR = 4.0 [95% CI: 2.2-7.2, p=1x10-4]), as well as higher rates of exposure to family or someone outside the household diagnosed with COVID-19, an annual household income < $40,000, living in rental housing, and chronic diseases. During the Executive Order in Michigan, African Americans, women, and the lowest income group reported worsening health behaviors and higher overall concern for the potential detrimental effects of the pandemic. The higher risk of contracting COVID-19 observed among African Americans may be due to the increased rates of working as essential employees, lower socioeconomic status, and exposure to known positive cases. Continued efforts should focus on COVID-19 prevention and mitigation strategies, as well as address the inequality gaps that result in higher risks for both short-term and long-term health outcomes.

Reconciling epidemiological models with misclassified case-counts for SARS-CoV-2 with seroprevalence surveys: A case study in Delhi, India

Underreporting of COVID-19 cases and deaths is a hindrance to correctly modeling and monitoring the pandemic. This is primarily due to limited testing, lack of reporting infrastructure and a large number of asymptomatic infections. In addition, diagnostic tests (RT-PCR tests for detecting current infection) and serological antibody tests for IgG (to assess past infections) are imperfect. In particular, the diagnostic tests have a high false negative rate. Epidemiologic models with a latent compartment for unascertained infections like the Susceptible-Exposed-Infected-Removed (SEIR) models can provide predictions for unreported cases and deaths under certain assumptions. Typically, the number of unascertained cases is unobserved and thus we cannot validate these estimates for a real study except for simulation studies. Population-based seroprevalence studies can provide a rough estimate of the total number of infections and help us check epidemiologic model projections. In this paper, we develop a method to account for high false negative rates in RT-PCR in an extension to the classic SEIR model. We apply this method to Delhi, the national capital region of India, with a population of 19.8 million and a COVID-19 hotspot of the country, obtaining estimates of underreporting factor for cases at 34-53 times and that for deaths at 8-13 times. Based on a recently released serological survey for Delhi with an estimated 22.86% seroprevalence, we compute adjusted estimates of the true number of infections reported by the survey (after accounting for misclassification of the antibody test results) which is largely consistent with the model outputs, yielding an underreporting factor for cases from 30-42. Together with the model and the serosurvey, this implies approximately 96-98% cases in Delhi remained unreported and whereas only 109,140 cases were reported on July 10, the true number of infections varied somewhere between 4.4-4.6 million across different estimates. While repeated serological monitoring is resource intensive, model-based adjustments, run with the most up to date data, can provide a viable option to keep track of the unreported cases and deaths and gauge the true extent of transmission of this insidious virus.

Understanding the patterns of repeated testing for COVID-19: Association with patient characteristics and outcomes

ImportanceThe diagnostic tests for COVID-19 have a high false negative rate, but not everyone with an initial negative result is re-tested. Michigan Medicine, being one of the primary regional centers accepting COVID-19 cases, provided an ideal setting for studying COVID-19 repeated testing patterns during the first wave of the pandemic. ObjectiveTo identify the characteristics of patients who underwent repeated testing for COVID-19 and determine if repeated testing was associated with patient characteristics and with downstream outcomes among positive cases. DesignThis cross-sectional study described the pattern of testing for COVID-19 at Michigan Medicine. The main hypothesis under consideration is whether patient characteristics differed between those tested once and those who underwent multiple tests. We then restrict our attention to those that had at least one positive test and study repeated testing patterns in patients with severe COVID-19 related outcomes (testing positive, hospitalization and ICU care). SettingDemographic and clinical characteristics, test results, and health outcomes for 15,920 patients presenting to Michigan Medicine between March 10 and June 4, 2020 for a diagnostic test for COVID-19 were collected from their electronic medical records on June 24, 2020. Data on the number and types of tests administered to a given patient, as well as the sequences of patient-specific test results were derived from records of patient laboratory results. ParticipantsAnyone tested between March 10 and June 4, 2020 at Michigan Medicine with a diagnostic test for COVID-19 in their Electronic Health Records were included in our analysis. ExposuresComparison of repeated testing across patient demographics, clinical characteristics, and patient outcomes Main Outcomes and MeasuresWhether patients underwent repeated diagnostic testing for SARS CoV-2 in Michigan Medicine ResultsBetween March 10th and June 4th, 19,540 tests were ordered for 15,920 patients, with most patients only tested once (13596, 85.4%) and never testing positive (14753, 92.7%). There were 5 patients who got tested 10 or more times and there were substantial variations in test results within a patient. After fully adjusting for patient and neighborhood socioeconomic status (NSES) and demographic characteristics, patients with circulatory diseases (OR: 1.42; 95% CI: (1.18, 1.72)), any cancer (OR: 1.14; 95% CI: (1.01, 1.29)), Type 2 diabetes (OR: 1.22; 95% CI: (1.06, 1.39)), kidney diseases (OR: 1.95; 95% CI: (1.71, 2.23)), and liver diseases (OR: 1.30; 95% CI: (1.11, 1.50)) were found to have higher odds of undergoing repeated testing when compared to those without. Additionally, as compared to non-Hispanic whites, non-Hispanic blacks were found to have higher odds (OR: 1.21; 95% CI: (1.03, 1.43)) of receiving additional testing. Females were found to have lower odds (OR: 0.86; 95% CI: (0.76, 0.96)) of receiving additional testing than males. Neighborhood poverty level also affected whether to receive additional testing. For 1% increase in proportion of population with annual income below the federal poverty level, the odds ratio of receiving repeated testing is 1.01 (OR: 1.01; 95% CI: (1.00, 1.01)). Focusing on only those 1167 patients with at least one positive result in their full testing history, patient age in years (OR: 1.01; 95% CI: (1.00, 1.03)), prior history of kidney diseases (OR: 2.15; 95% CI: (1.36, 3.41)) remained significantly different between patients who underwent repeated testing and those who did not. After adjusting for both patient demographic factors and NSES, hospitalization (OR: 7.44; 95% CI: (4.92, 11.41)) and ICU-level care (OR: 6.97; 95% CI: (4.48, 10.98)) were significantly associated with repeated testing. Of these 1167 patients, 306 got repeated testing and 1118 tests were done on these 306 patients, of which 810 (72.5%) were done during inpatient stays, substantiating that most repeated tests for test positive patients were done during hospitalization or ICU care. Additionally, using repeated testing data we estimate the "real world" false negative rate of the RT-PCR diagnostic test was 23.8% (95% CI: (19.5%, 28.5%)). Conclusions and RelevanceThis study sought to quantify the pattern of repeated testing for COVID-19 at Michigan Medicine. While most patients were tested once and received a negative result, a meaningful subset of patients (2324, 14.6% of the population who got tested) underwent multiple rounds of testing (5,944 tests were done in total on these 2324 patients, with an average of 2.6 tests per person), with 10 or more tests for five patients. Both hospitalizations and ICU care differed significantly between patients who underwent repeated testing versus those only tested once as expected. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients. Key PointsO_ST_ABSQuestionC_ST_ABSDoes having repeated diagnostic tests for the novel coronavirus (COVID-19) depend on patient characteristics and disease outcomes? FindingsThis cross-sectional study of testing patterns with 15,920 patients tested for SARS-CoV-2 virus at Michigan Medicine found significant differences in testing rates across patient age, body mass index, sex, race/ethnicity, neighborhood poverty level, prior history of circulatory diseases, any cancer, Type 2 diabetes, kidney, and liver diseases. Higher hospitalization rates and intensive care unit admissions were associated with repeated testing as expected. MeaningThe results of this study describe diagnostic testing patterns for the novel COVID-19 virus at Michigan Medicine, and how they relate to patient characteristics and COVID-19 outcomes.

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

BackgroundWe perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race. MethodsThe study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center. ResultsPre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. ConclusionsUnderstanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Are women leaders significantly better at controlling the contagion?

Recent media articles have suggested that women-led countries are doing better in terms of their responses to the COVID-19 pandemic. We examine an ensemble of public health metrics to assess the control of COVID-19 epidemic in women- versus men-led countries worldwide based on data available up to June 3. The median of the distribution of median time-varying effective reproduction number for women- and men-led countries were 0.89 and 1.14 respectively with the 95% two-sample bootstrap-based confidence interval for the difference (women - men) being [- 0.34, 0.02]. In terms of scale of testing, the median percentage of population tested were 3.28% (women), 1.59% (men) [95% CI: (-1.29%, 3.60%)] with test positive rates of 2.69% (women) and 4.94% (men) respectively. It appears that though statistically not significant, countries led by women have an edge over countries led by men in terms of public health metrics for controlling the spread of the COVID-19 pandemic worldwide. One Sentence SummaryWe quantitatively compare countries led by women with countries led by men in terms of public health metrics for controlling the spread of the novel coronavirus.

Lockdown Effect on COVID-19 Spread in India: National Data Masking State-Level Trends

IntroductionIndia has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. MethodsUsing data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, we illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case-fatality rates, doubling times of cases, effective reproduction numbers, and the scale of testing. ResultsThe estimated effective reproduction number R for India was 3.36 (95% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. ConclusionsPatterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualization tools that are daily updated at covind19.org.

Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms

ImportanceIndia has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India - a democracy of 1.34 billion people - took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3rd, soon after the analysis of this paper was completed. ObjectiveTo study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic. Design, Setting, and ParticipantsWe use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia. Main Outcome and MeasuresThe current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI). ResultsOur predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in peoples adherence to a 21-day lockdown (March 25 - April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under "no intervention" and "social distancing and travel bans without lockdown" are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being -0.185 [-0.548, 0.236] for January, -0.110 [-0.362, 0.157] for February, and -0.173 [-0.314, -0.026] for March. Conclusions and RelevanceThe lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies. SoftwareOur contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.