IJID Regions

Seroepidemiology and genomics are valuable tools to investigate the transmission of COVID-19. We utilized qRT-PCR, serum antibody immunoassays, and whole genome sequencing to examine the spread of SARS-CoV-2 infections in North East (NE) region of India during the first and second pandemic waves (June 2020 to September 2021). qRT-PCR analysis was performed on a selected population from NE India during June 2020 to July 2021, and metadata were collected for the region. Seroprevalence and neutralizing antibody immunoassay were studied on selected individuals (n=2026) at three time points (August 2020, February 2021 and June 2021), as well as in a cohort (n=35) for a year (August 2020 to August 2021). SARS-CoV-2 genomes of 914 qRT-PCR positive samples (June 2020 to September 2021) were sequenced and assembled, and those obtained from the sequence databases were analyzed. Test positivity rates in first and second waves were 6.34% and 6.64% in the state of Assam, respectively, and a similar pattern was observed in other NE states. Seropositivity in August 2020, February 2021, and June 2021 were 10.63%, 40.3% and 46.33% respectively, and neutralizing antibody prevalence were 90.91%, 52.14%, and 69.30% respectively. The cohort group showed the presence of stable neutralizing antibody throughout the year. Normal variants dominated the first wave, while the variant of concerns (VOCs) B.1.617.2 and AY-sublineages dominated the second wave, and identified mostly among vaccinated individuals. All eight states of NE India reported numerous incidences of SARS-CoV-2 VOCs, especially B.1.617.2 and AY sublineages, and their prevalence co-related well with high TPR and seropositivity rate in the region. High infection and seroprevalence of COVID-19 in NE India during the second wave was associated with the emergence of VOCs. Natural infection prior to vaccination provided higher neutralizing activity than vaccination alone.

Background & ObjectiveSocial determinants of evolving covid-19 pandemic have not been well studied. To determine trends in transition of this epidemic in India we performed a study in states at various levels of human development index (HDI). MethodsWe used publicly available data sources to track progress of covid-19 epidemic in India in different states and territories where it was reported in significant numbers. The states (n=20) were classified into tertiles of HDI and weekly trends in cases and deaths plotted from 15 March to 2 May 2020. To assess association of HDI with state-level covid-19 burden we performed Pearsons correlation. Logarithmic trends were evaluated for calculation of projections. A microlevel study was performed in select urban agglomerations for identification of socioeconomic status (SES) differentials. ResultsThere is wide regional variation in covid-19 cases and deaths in India from mid-March to early-May 2020. High absolute numbers have been reported from states of Maharashtra, Gujarat, Delhi, Madhya Pradesh, Rajasthan and Tamilnadu. Growth rate in cases and deaths is slow in high HDI states while it has increased rapidly in middle and lower HDI states. In mid-March 2020 there was a strong positive correlation of state-level HDI with weekly covid-19 cases (r= 0.37, 0.40) as well as deaths (r= 0.31, 0.42). This declined by early-May for cases (r= 0.04, 0.06) as well as deaths (r= - 0.005, 0.001) with significant negative logarithmic trend (cases R squared= 0.92; deaths R squared= 0. 84). These trends indicate increasing cases and deaths in low HDI states. Projection reveals that this trend is likely to continue to early-June 2020. Microlevel evaluation shows that urban agglomerations are major focus of the disease in India and it has transited from middle SES to low SES locations. ConclusionThere is wide variability in burden of covid-19 in India. Slow growth and flattening of curve is observed in high-HDI states while disease is increasing in mid and lower HDI states. Projections reveal that lower HDI states would achieve parity with high HDI states by early-June 2020. Covid-19 is mostly present in urban agglomerations where it has transited from upper-middle to low SES locations. Public health strategies focusing on urban low SES locations and low HDI states are crucial to decrease covid-19 burden in India.

To understand the effect of nationwide lockdown on transmissibilty of SARS-CoV-2 in India, time varying reproduction number during the first weeks of April, 2020 was estimated. The time varying reproduction number was estimated using EpiEstim package in R programming language. The reproduction number has come down significantly during the lockdown period both at national level and in most states but it wasnt reduced to less than 1. This calls for urgent need for more effective control measures in addition to lockdown to stop the epidemic spread of the virus.

Conflicting reports on the persistence of antibody levels in individuals recovered from COVID-19 infection, suggest that the immunity against COVID-19 may not be lasting for long. In India, by 30th June, 2021, not less than 30 million people were infected with COVID-19 and 0.39 million people were reported to have lost their life to the disease in India. I the current study we followed up with a subsample of our previous sero-survey participants to assess whether natural immunity against SARS-CoV-2 was associated with a reduced risk of re-infection. We conducted telephonic interview of a total of 3038 participants, out of which 2238 participants responded and 5 participants were found to be not alive, as conveyed by their close relatives. There was a non-response rate of 26.1%. Out of the 2238 participants, 1170 were sero-positive and 1068 were sero-negative for antibody against COVID-19. Our survey found that only 3 individuals in the sero-positive group got infected with COVID-19 whereas 127 individuals reported contracting the infection the sero-negative group. Interestingly, from the 127 sero-negative individuals who later contracted COVID-19 infection, 30 needed hospitalization, out of which 12 were on oxygen therapy, four in ICU and one was on ventilator. At the other hand, from the 3 sero-positives re-infected with COVID-19, one had hospitalization, but didnnot require oxygen support or critical care. These findings reinforce the strong plausibility that development of antibody following natural infection not only protects against re-infection by the virus to a great extent, but also safeguards against progression to severe COVID-19 disease.

It is crucial to know whether a single dose of vaccine against SARS-CoV-2 is sufficient to elicit immune response in previously infected people in India. A total of 121 participants (baseline seropositive 46 and seronegative 75) were included to study the immune response to ChAdOx1-nCOV (Covishield) vaccine in previously infected or uninfected people. IgG antibodies were estimated at three different time intervals, i.e. pre-vaccination, 25-35 days post 1st vaccination and 25-35 days post 2nd vaccination. The IgG antibody titre was significantly high among previous seropositive subjects with single dose of vaccine compared to seronegative group with both doses of vaccine respectively (4.59{+/-}1.04 vs 3.08{+/-}1.22, p-value: <0.0001). In conclusion, a single dose of Covishield(R) vaccine might be sufficient to induce an effective immune responsein subjects with prior SARS-CoV2 infection. Stratifying vacinees based on their SARS-CoV2 IgG antibody titre before vaccination would help in meeting the increasing vaccine demand and could be effective to circumvent further wave of the pandemic in India.

ObjectiveThe objective of this study is to evaluate the association with different factors empirically found to affect the spread and the severity of Covid-19. Evidently there is less likelihood of having one single and absolute solution to this pandemic. It is pragmatic to look for a multi-pronged and collaborative assembly of probable solutions, which is the higher objective of this study. DesignEcological study. SettingGlobal setting including 45 countries from all six inhabited continents PopulationTwo (2) or three (3) countries from each geographical region of the continents selected on the basis of population Main outcomemeasures correlation factors derived from comparisons between different sets of variables ResultsEmpirical trends suggested in the existing literature were quantified in a global setting establishing clear trends. Correlation between the proportion of the population affected and median age, prime climate zones, malaria and tuberculosis incidence, BCG coverage and mitigation measures were established. ConclusionsThe study findings suggest that demographic and climatological factors, high endemicity of TB and Malaria, and universal BCG programmes may have a cushioning effect in the impact of Covid-19 on health systems of poorer and developing nations. In the light of these findings more emphasis is necessary on the protective effects of BCG and antiviral properties of antimalarial drugs. BackgroundThe coronavirus disease (Covid-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected 213 countries as of 20th May 2020, with a total of 5,017,883 total cases reported globally, and has caused 325,624 reported deaths till date (worldometers.info). It began with the report of an outbreak of pneumonia of unknown cause in Wuhan, China on 31st December 2019. This outbreak was declared a Public Health Emergency of International Concern on 30th January 2020. Even as early as 18th February 2020, it was noted that inspite of lower case fatality rates Covid-19 had already resulted in more deaths than SARS and MERS combined.1 On the 11th of March the WHO made the assessment that Covid-19 could be classified as a pandemic. A systematic review found the basic reproduction number between 2.0 - 3.0.2 According to the WHO, the crude case fatality rate is 3%, with 15% of those affected suffering from severe disease and 5% critical.3 As developed countries with highly ranked health systems report high burden of cases straining health system capacities, concerns grow about the impact on resource constrained health systems in developing and underdeveloped nations. A better understanding of the disease epidemiology could help in planning the pandemic response, both in terms of resource allocation and mitigation measures in areas and populations more likely to be affected. In the second week of April, 2020, the IMF also was reported to be keen to know the reason behind the lower impact of Covid-19 in African and Asian countries; this also served as a driving factor behind this study.

BackgroundCoronavirus disease 2019 (COVID-19) has emerged as a pandemic, and the infection due to SARSCoV-2 has now spread to more than 200 countries3. Surveillance systems form the foundation stone of active case finding, testing and contact tracing, which are the key components of the public health response to this novel, emerging infectious disease4. There is uncertainty about the true proportion of patients who remain asymptomatic or pre-symptomatic at a given time. As per the WHO-China Joint Monitoring Mission Report, and an analysis of 21 published reports, anywhere between 5 and 80 per cent of SARS-CoV-2-infected patients have been noted to be asymptomatic5, 6 Whereas in India 4197563 cases are positive, in which in West Bengal total 180788 cases(4.04% of Cases of India) positive of COVID 19. In Paschim Medinipur (West Medinipur) district contributing total 5489 cases (3.03% cases of West Bengal)9,10,11. In this scenario, we want to know the status of IgG seroprevalence of SARS-CoV-2 among asymptomatic general population, so that we can determine the extent of infection of SARS-CoV-2 in general population. ObjectivesPrimary Objective:- To estimate the seroprevalence for SARS-CoV-2 infection in the general asymptomatic population at Paschim Medinipur District. Secondary Objectives-To estimate age and sex specific seroprevalence. To determine the socio demographic risk factors for SARS-CoV-2 infection; To determine the other risk factors like comorbidities, vaccination status, travel history, contact history etc.; To determine the durability of Immunity (IgG) conferred by natural infection of SARS-CoV-2 in individuals previously RTPCR positive. MethodologyIt was a cross sectional 30 cluster study among the population of Paschim Medinipur district of West Bengal conducted in last week of July and 1st week of August 2020 among 458 asymptomatic general population and 30 RTPCR positive cases in 30 villages or wards of municipalities. 30 clusters were chosen from list of COVID 19 affected villages/wards of municipality as per PPS (Probability Proportional to Size) method. ResultsOf the 458 asymptomatic general population,19 asymptomatic people found to be seropositive IgG for SARS-CoV-2 with Mean or average total seropositivity rate of 4.15%. 19 Out of 30 (63.33%) RTPCR positive patients found Seronegative. Median of Days between RTPCR test and sero negativity found was 60 with minimum 28 days to maximum 101 days and Range of 73 days and a standard deviation of 19.46. Among risk factors, the risk of having IgG is more in persons having Travel history with odds ratio of 2.99-95%CI (1.17-7.65) with p-value-0.02. Hydroxychloroquine prophylaxis with Odds ratio of 8.49-95% CI(1.59-45.19) with p value - 0.003. Occupation as migrant labour with Odds ratio of 5.08-95% CI(1.96-13.18) with p value of 0.001. H/O Chicken pox with Odds ratio of 2.15-95% CI(0.59-7.79) with p value of 0.017. Influenza vaccinated with Odds ratio of 8.07 with 95% CI (0.8-81.48) with a p value of 0.036. ConclusionOf the 458 asymptomatic general population,19 asymptomatic people found to be seropositive IgG for SARS-CoV-2 with Mean or average total seropositivity rate of 4.15%. 19 Out of 30 (63.33%) RTPCR positive patients found Seronegative. Median of Days between RTPCR test and sero negativity found was 60 with minimum 28 days to maximum 101 days and Range of 73 days and a standard deviation of 19.46. Those having Travel History and having occupation as Migrant Labourer - have significantly higher probability of getting infected with SARS-CoV-2. No role has been found of Hydroxychloroquine Medicines as Chemoprophylactic. No durable immunity conferred by natural infection with SARS-CoV-2 -mean time to become seronegative after positive RTPCR test 60 days. So there is a chance of reinfection after average 2 months.

In COVID 19 Pandemic,first line of defence is effective vaccination program.Because of multiple platforms available for vaccine production we tested relative immunogenicity of two vaccines available in India, Covaxin(R) and Covishield(R) We performed quantitative analysis of neutralizing antibodies to SARS Cov2 spike (receptor binding domain) protein, from sera of 53 subjects who completed vaccines schedules.There was significantly higher immunogenic response with Covishield(R) as compared to Covaxin(R) and are independent of age. Studies on a large scale with long term follow up are needed to further advance the knowledge in this domain.

ImportanceHigher risks of contracting infection, developing severe illness and mortality are known facts in aged and male sex if exposed to the wild type SARS-CoV-2 strains (Wuhan and B.1 strains). Now, accumulating evidence suggests greater involvement of lower age and narrowing the age and sex based differences for the severity of symptoms in infections with emerging SARS-CoV-2 variants. Delta variant (B.1.617.2) is now a globally dominant SARS-CoV-2 strain, however, current evidence on demographic characteristics for this variant are limited. Recently, delta variant caused a devastating second wave of COVID-19 in India. We performed a demographic characterization of COVID-19 cases in Indian population diagnosed with SARS-CoV-2 genomic sequencing for delta variant. ObjectiveTo determine demographic characteristics of delta variant in terms of age and sex, severity of the illness and mortality rate, and post-vaccination infections. DesignA cross sectional study SettingDemographic characteristics, including vaccination status (for two complete doses) and severity of the illness and mortality rate, of COVID-19 cases caused by wild type strain (B.1) and delta variant (B.1.617.2) of SARS-CoV-2 in Indian population were studied. ParticipantsCOVID-19 cases for which SARS-CoV-2 genomic sequencing was performed and complete demographic details (age, sex, and location) were available, were included. ExposuresSARS-CoV-2 infection with Delta (B.1.617.2) variant and wild type (B.1) strain. Main Outcomes and MeasuresThe patient metadata containing details for demographic and vaccination status (two complete doses) of the COVID-19 patients with confirmed delta variant and WT (B.1) infections were analyzed [total number of cases (N) =9500, Ndelta=6238, NWT=3262]. Further, severity of the illness and mortality were assessed in subsets of patients. Final data were tabulated and statistically analyzed to determine age and sex based differences in chances of getting infection and the severity of illness, and post-vaccination infections were compared between wild type and delta variant strains. Graphs were plotted to visualize the trends. ResultsWith delta variant, in comparison to wild type (B.1) strain, higher proportion of lower age groups, particularly <20 year (0-9 year: 4.47% vs. 2.3%, 10-19 year: 9% vs. 7%) were affected. The proportion of women contracting infection were increased (41% vs. 36%). The higher proportion of total young (0-19 year, 10% vs. 4%) (p=.017) population and young (14% vs. 3%) as well as adult (20-59 year, 75% vs. 55%) women developed symptoms/hospitalized with delta variant in comparison to B.1 infection (p< .00001). The mean age of contracting infection [Delta, men=37.9 ({+/-}17.2) year, women=36.6 ({+/-}17.6) year; B.1, men=39.6 ({+/-}16.9) year and women= 40.1 ({+/-}17.4) year (p< .001)] as well as developing symptoms/hospitalization [Delta, men=39.6({+/-} 17.4) year, women=35.6 ({+/-}16.9) year; B.1, men=47({+/-}18) year and women= 49.5({+/-}20.9) year (p< .001)] was considerably lower. The total mortality was about 1.8 times higher (13% vs. 7%). Risk of death increased irrespective of the sex (Odds ratio: 3.034, 95% Confidence Interval: 1.7-5.2, p<0.001), however, increased proportion of women (32% vs. 25%) were died. Further, multiple incidences of delta infections were noted following complete vaccination. Conclusions and RelevanceThe increased involvement of young (0-19 year) and women, lower mean age for contracting infection and symptomatic illness/hospitalization, higher mortality, and frequent incidences of post-vaccination infections with delta variant compared to wild type strain raises significant epidemiological concerns. Key PointsO_ST_ABSQuestionC_ST_ABSDid SARS-CoV-2 B.1.617.2 (Delta) variant infections show varied demographic characteristics in comparison to wild type strains? FindingsIn this cross sectional study viral genomic sequences of 9500 COVID-19 patients were analyzed. As the key findings, increased involvement of young (0-19 year) and women, lower mean age for contracting infection and symptomatic illness/hospitalization, higher mortality, and frequent incidences of post-vaccination infections with delta variant in comparison to wild type (WT) strain (B.1) were observed. MeaningThe findings of this study suggest that delta variant has varied demographic characteristics reflecting increased involvement of the young and women, and increased lethality in comparison to wild type strains.

BackgroundSince the COVID-19 pandemic hit Indian states at varying speed, it is crucial to investigate the geographical pattern in COVID-19. We analyzed the geographical pattern of COVID-19 prevalence and mortality by the phase of national lockdown in India. MethodUsing publicly available compiled data on COVID-19, we estimated the trends in new cases, period-prevalence rate (PPR), case recovery rate (CRR), and case fatality ratio (CFR) at national, state and district level. FindingsThe age and sex are missing for more than 60 percent of the COVID-19 patients. There is an exponential increase in COVID-19 cases both at national and sub-national levels. The COVID-19 infected has jumped about 235 times (from 567 cases in the pre-lockdown period to 1,33,669 in the fourth lockdown); the average daily new cases have increased from 57 in the first lockdown to 6,482 in the fourth lockdown; the average daily recovered persons from 4 to 3,819; the average daily death from 1 to 163. From first to the third lockdown, PPR (0.04 to 5.94), CRR (7.05 to 30.35) and CFR (1.76 to 1.89) have consistently escalated. At state-level, the maximum number of COVID-19 cases is found in the states of Maharashtra, Tamil Nadu, Delhi, and Gujarat contributing 66.75 percent of total cases. Whereas no cases found in some states, Kerela is the only state flattening the COVID-19 curve. The PPR is found to be highest in Delhi, followed by Maharastra. The highest recovery rate is observed in Kerala, till second lockdown; and in Andhra Pradesh in third lockdown. The highest case fatality ratio in the fourth lockdown is observed in Gujarat and Telangana. A few districts viz. like Mumbai (96.7); Chennai (63.66) and Ahmedabad (62.04) have the highest infection rate per 100 thousand population. Spatial analysis shows that clusters in Konkan coast especially in Maharashtra (Palghar, Mumbai, Thane and Pune); southern part from Tamil Nadu (Chennai, Chengalpattu and Thiruvallur), and the northern part of Jammu & Kashmir (Anantnag, Kulgam) are hot-spots for COVID-19 infection while central, northern and north-eastern regions of India are the cold-spots. ConclusionIndia has been experiencing a rapid increase of COVID-19 cases since the second lockdown phase. There is huge geographical variation in COVID-19 pandemic with a concentration in some major cities and states while disaggregated data at local levels allows understanding geographical disparity of the pandemic, the lack of age-sex information of the COVID-19 patients forbids to investigate the individual pattern of COVID-19 burden. Major highlights of the studyO_LIThe new cases of COVID-19 have increased exponentially since the second lockdown phase in India. There is consistent improvement in the recovery rate (CRR is 7.1 percent in pre-lockdown to 44.0 percent in fourth lockdown period) with a low level of CFR (1.87 percent as of May 29st 2020). C_LIO_LIAt the state level, the most vulnerable states for the COVID-19 crisis are the state of Maharashtra, Tamil Nadu, Delhi, and Gujarat contributing 66.75 percent of total cases. C_LIO_LIThe PPR is found to be highest in Delhi, followed by Maharastra. While the highest recovery rate is observed in Kerala, the highest case fatality ratio in the fourth lockdown is observed in Gujarat and Telangana. The top 10 hotspot districts in India account for 58.3 percent of the new cases. Among them, Mumbai has the highest infection rate of 96.77 per 100 thousand, followed by Chennai with 63.66 per 100 thousand, and Ahmedabad with 62.04 per 100 thousand. C_LIO_LIThe information on age and sex are missing for more than 60 percent of the patients. C_LI

India had witnessed unprecedented surge in SARS-CoV-2 infections and the dire consequences during the second wave of COVID-19, but the detailed report of the epidemiological based spatiotemporal incidences of the disease is missing. Here in, we have applied various statistical methods like correlation, hierarchical clustering to know the pattern of pathogenesis of the circulating VoCs. B.1.617.1 (Kappa) was the predominant VoC during the early phase of second wave. Delta (B.1.617.2) or Delta-like (AY.x) VoC constitutes majority (>90.17) of the cases during the peak of second wave. The correlation plot showed Delta/Delta-like lineage is inversely correlated with other lineages including B.1.617.1 (kappa), B.1.1.7, B.1, B.1.36.29 and B.1.36. Delta/Delta-like surge coincided with second wave whereas all other lineages (B.1.617.1, B.1.36.29, etc.) occurred during the prior phase of the second wave. The spatiotemporal analysis showed that most of the Indian states were affected during the peak of the second wave due to delta surge and fall under the same cluster. The second cluster populated mostly by north-eastern states and islands of India were minimally affected. The presence of signature mutations (T478K, D950N, E156G) along with L452K, D614G and P681R within the spike protein of Delta or Delta-like might cause elevation in host cell attachment, increased transmission and altered antigenicity which in due course of time has replaced the other circulating variants. The timely assessment of new VoCs will provide a rationale for updating the diagnostic, vaccine development by medical industries and decision making by various agencies including government, educational institutions, and corporate industries.

Severity of Covid-19 diseases has been disproportionate with higher case-fatality ratio affecting developed nations. In India, states with higher income have reported more number of deaths compared to lower income states. The global burden of diseases India 2019 and the National Health Profile 2019 data was used to draw correlations with Covid-19 mortality at two different dates of peak Covid-19 cases in India. We explored correlation of mortality in different states of India with prevalence of different diseases, demography, development, sanitation etc. The study found a positive correlation with known demographic parameters such as percentage of elderly population(spearman correlation coefficient(rho) =0.44 and 0.46 with 1st and 2nd peak respectively). Similarly, percentage urbanization was seen to correlate well with mortality(rho=0.71 and 0.57) suggesting Covid-19 to be a predominantly urban disease. Prevalence of Autoimmune diseases, and Cancer show higher correlation with deaths. A surprising positive correlation emerged between improved sanitation parameters, such as closed drainage and indoor toilets, with COVID-19 deaths. Overall the multivariate regression model achieved by combining demography, sanitation, autoimmune diseases and cancer gave us the best prediction for Covid-19 mortality(adjusted R square value of 0.71 with peak 1 and 0.85 with peak 2). Analysis of the Covid-19 related data seems to indicate that as the wealth of a state increases, the states urban landscape changes often leading to better sanitation facilities. The lifestyle and prevalence to autoimmune diseases as well as cancer also increases. However, this may affect the states ability to fight pandemics due to lower exposure to pathogens and immune training.

OBJECTIVEThe research aimed to evaluate the attitude and perceptions towards the covid-19 vaccine among Hepatitis B and C patients in Peshawar, Khyber Pakhtunkhwa, Muzaffarabad, Azad Kashmir, Pakistan. METHODSA survey-based study was adopted to evaluate the attitude of Hepatitis B and C patients towards immunization against covid-19 in Peshawar (KPK) and Muzaffarabad (AJK) cities of Pakistan. The study continued from January 2020 to February 2021. Participants were also assessed for their perception towards covid-19 vaccination. RESULTSA total of 839 (33.6%) individuals participated in the study. About 52 % of Hepatitis B patients were immunized against Covid-19, whereas the number of Hepatitis C patients was recorded at around 48%. About 53.7 % of participants refused to get the vaccine without any reason. About 63.2% of patients showed concern about the insufficient data available on the vaccine safety and efficacy published by the Public Health Department. Individuals with higher education were observed to be more open towards vaccination then those without a formal education. More than half of the participants (61.5 %) were concerned about the interference of the vaccine with their hepatitis treatment whereas 54.7 % patients refused vaccine because of a poor liver condition. CONCLUSIONSThe data indicated that limited data availability regarding the vaccine efficacy in viral hepatitis patients and negative attitudes of people toward covid-19 vaccination is the main cause of Covid-19 vaccination refusal among hepatitis B and C patients. DESCRIPTORSHepatitis B, Hepatitis C, covid-19, immunization, vaccine refusal, Pakistan.

BackgroundIndia is the second-largest population in the world, and it is not well equipped, hitherto, in the scenario of the global pandemic, SARS-CoV-2 could impart a devastating impact on the Indian population. Only way to respond against this critical condition is by practicing large-scale social distancing. India lock down for 21 days, however, till 7 April 2020, SARS- CoV-2 positive cases were growing exponentially, which raises the concerns if the number of reported and actual cases are similar. MethodsWe use Lasso Regression with = 0.12 and Polynomial features of degree 2 to predict the growth factor. Also, we predicted Logistic curve using the Prophet Python. Further, using the growth rate to logistic, and carrying capacity is 20000 allowed us to calculate the maximum cases and new cases per day. ResultsWe found the predicted growth factor with a standard deviation of 0.3443 for the upcoming days. When the growth factor becomes 1.0, which is known as Inflection point, it will be safe to state that the rate is no longer exponential. The estimated time to reach the inflection point is between 15-20 April. At that time, the estimated number of total positive cases will be over 12500, if lockdown remains continue. ConclusionsOur analysis suggests that there is an urgent need to take action to extend the period of lockdown and allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. Otherwise, the outbreak in India can reach the level of the USA or Italy or could be worse than these countries within a few days or weeks, given the size of the population and lack of resources.

BackgroundKidney transplant recipients (KTRs) were given a 3-dose primary series of COVID-19 vaccination as they were vulnerable to infection due to immunosuppression. MethodsThis study was a longitudinal evaluation of nAB dynamics in 43 KTRs in a low-middle income setting receiving 3-dose homologous (mRNA-1273- mRNA-1273- BNT162b2) vaccination against COVID-19. Samples were obtained at time-points (TP) 0- pre-vaccination, TP1- 1 month post first dose(mRNA-1273), TP2-1-month post second dose (mRNA-1273), TP3- 4 months post-second dose, TP4- 2 weeks post-third dose(BNT162b2), TP5-5 months post-third dose and TP6-12 months-post third dose. Anti-SARS-CoV-2 nAB were detected using Genscript cPassTM pseudoviral neutralization kit. Demographic and clinical details were obtained through interviewer administered questionnaires. ResultsPre-vaccination serum analysis showed n=7 KTRs had prior COVID-19 infection, classified as infected+vaccinated, while others were vaccinated. Both groups were similar in age(41.7years vs 46.7years,p=0.2383), gender, and transplant characteristics. Seroconversion and MAB in the vaccinated and infected+vaccinated KTRs were:TP1-8.3% vs 100%(p<0.001), MAB-64.3IU/ml vs 1424IU/ml(p=0.0167TP2-52.7% vs 100%(p=0.0194), MAB-175IU/ml vs 2790IU/ml(p<0.0001), TP3-100% vs 100%, MAB-106IU/ml vs 2153IU/ml(p=0.0002), TP4-100% vs 100%, MAB-736 IU/ml vs 2152IU/ml(p=0.0307) and TP6-100% vs 100%, MAB >2565IU/ml vs >3028IU/ml(p=0.5238) No factors were associated with seroconversion or MAB. ConclusionKTRs receiving a three-dose mRNA COVID-19 vaccine regime maintained strong nAB levels at one-year follow-up, with comparable antibody levels seen between KTRs with prior infection + vaccination and vaccination alone.

The coronavirus (COVID-19) is spreading rapidly across the country but Indias testing regime is far from the global standards. It is important to identify the states where testing needs expansion and the magnitudes of active COVID cases are higher focusing on current health infrastructure to meet the pandemic. The data on COVID-19 was extracted from the Application Programming Interface. Test positive rate, test per confirmed case, recovery rate, case fatality rate, and percent distribution of active cases were computed. Availability of hospitals, hospital beds, intensive care unit and ventilators per lakh population was also computed by public and private sector. The result revealed that, Maharashtra constitutes more than one-third positive cases in the country. More than a quarter of the active cases in India belonged to the Mumbai district of Maharashtra, followed by the Chennai district (9.4%) and Ahmedabad district (9.1%). Further, about 40 percent of the active cases in India belonged to the 11 districts of Maharashtra. The increased test positive rate in Maharashtra and Gujarat to almost double in last one month is a concern. In order to bring the states and the country in right track, the test positive rate need to be brought down to below 2 percent. The procurement of higher number of high throughput machine, the Cobas 6800 testing machine, is need of the hour. Only few states have adequate health infrastructure. The priority should be the laid on expansion of more laboratories and hospitals, storage of PPE kit, testing kit, and indigenously developed vaccines. HighlightsO_LIMaharashtra is having the highest number of positive cases followed by Gujarat and Tamil Nadu. Maharashtra constitutes more than one-third positive cases in the country, but the test per confirmed cases (8) is much lower than the other states. C_LIO_LIMore than a quarter of the active cases in India belonged to the Mumbai district (26.1%) of Maharashtra, followed by the Chennai district (9.4%) and Ahmedabad district (9.1%). Further, about 40 percent of the active cases in India belonged to the 11 districts of Maharashtra. C_LIO_LIThe test positive rate is higher in Maharashtra, Gujarat and Delhi is a concern. C_LIO_LIThe recovery rate in India increased substantially by 26.5 percent point from 11.9 percent on April 14 to 38.4 percent on May 17, 2020. C_LIO_LIThe case fatality rate of Covid-19 in India declined by 0.2 percent from 3.4 percent on April 14 to 3.2 percent on May 17 in India. C_LIO_LIThe number of Dedicated Covid Hospitals is not sufficient in India. C_LIO_LIThe available ventilators in the country will deficit in near future to cater to a growing number of active Covid-19 patients and the burden of other communicable and non-communicable diseases. C_LIO_LIIndia has only 569 testing laboratories (396 govt. and 173 private) against its 1.35 billion population. The procurement of higher number of high throughput machine, the Cobas 6800 testing machine, is need of the hour. C_LI

ObjectivesThe comparable effectiveness of Covishield and Covaxin vaccines has not been studied. We compared the effectiveness of Covishield and Covaxin vaccines against moderate to severe COVID-19. MethodsIn this retrospective observational study, we collected data of patients who were admitted with moderate to severe COVID-19. The vaccination status and comorbidities of the patients were documented. The incidence and in-hospital mortality of COVID-19 patients was assessed. Univariate analysis was performed to determine the risk factors of in-hospital mortality. ResultsOf 294 patients, 5.1% (n=15) received Covaxin and 26.5% (n=78) received Covishield; 68.4% (n=201) patients were unvaccinated. Of patients who were vaccinated and contracted COVID-19, 24.8% (n=73) had taken the first dose and 6.8% (n=20) had taken the second dose of either vaccine. The in-hospital mortality rate was 13.6% (n=40). 24/40 (60%) people who had hospital mortality were unvaccinated.3/40(7.5%) had succumbed to death after receiving double dose of Covishield, 11/40 (27.5%) had succumbed to death after receiving single dose of Covishield, 2/40(5%) had succumbed to death after receiving single dose of Covaxin, none had reported infection after receiving second dose of Covaxin. No significant association was found with the type of vaccine and the in-hospital mortality (p=0.23). Significant associations with in-hospital mortality were found with the interval before COVID-19 disease and vaccination (OR, 3.02; p=0.01); and the presence of diabetes mellitus (OR, 2.13; p=0.02), cardiovascular diseases (OR, 2.11; p<0.001), and malignancy (OR: 2.33; p=0.0325). ConclusionThere was no significant difference in the effectiveness of Covaxin and Covishield in terms of the incidence of COVID-19 and in-hospital mortality. Diabetes mellitus, cardiovascular diseases, and malignancies had a significant association with in-hospital mortality in patients with moderate to severe COVID-19.

BackgroundIn January 2021, India drug regulator issued restricted emergency approval for COVAXIN and COVISHIELD which were manufactured in India. On mid-January 2021, in India, there were 10.5million confirmed cases and 0.15 million deaths. ObjectivesThe objectives were to evaluate vaccine effectiveness (VE) of India made Covid-19 vaccines against SARS-CoV-2 infection. MethodsA test negative case control study was conducted from May 2021 to December 2021 for duration of 8months among people attending an RT-PCR centre at a medical college Hospital for RT-PCR test. The baseline characteristics and RT-PCR report; and preliminary data about vaccine status were collected from the RT-PCR centre. The exposure to vaccination was enquired via Phone call or was checked with data available with the health authorities. ResultsAfter applying inclusion exclusion criteria, case and control definitions, a total of 380 participants (95cases and 285 controls) were included. The adjusted VE of two doses of COVISHIED vaccine against symptomatic SARS-CoV-2 infection was 52.2% (95% CI, 41.7 to 62.1) and single dose was 40.88% (95% CI, 31.26 to 51.29). The adjusted VE of two doses of COVAXIN vaccine against SARS-CoV-2 infection was 39% (95% CI, 29.40 to 49.27). The overall VE was 48.20% (95% CI, 37.90 to 58.22) for two doses of any vaccines. ConclusionsIndia made vaccines were nearly 50% effective. Similar results show by different studies with a margin of 10-25% difference. Further new studies should be conducted as new variants of SARS-CoV-2 are emerging, and we dont know how the vaccine works against the variants and booster doses were required or not.

In absence of extensive testing for SARS-CoV-2, true prevalence of COVID-19 cases in India remain unknown. In this study, a conservative estimate of prevalence of COVID-19 is calculated based on the age wise COVID-19 positivity rate among patients with severe respiratory illness as reported by Indian Council of Medical Research. Calculations in the study estimates a cumulative number of 17151 COVID-19 positive cases by the end of April 2, 2020.

IntroductionSome hemodialysis patients are reluctant to COVID-19 for the development of adverse events (AEs). The aim of this study was to verify the safety of mRNA-1273 vaccine in hemodialysis patients. MethodsWe conducted a retrospective analysis of in-center hemodialysis patients who underwent mRNA-1273 vaccine from March 1st to April 30th, 2021. All AEs occurring after the first and the second doses were collected and classified as local or systemic. ResultsOverall, 126 patients on chronic maintenance dialysis without a prior COVID-19 diagnosis were vaccinated with two doses of mRNA-1273 vaccine. Mean age was 68 (IQR, 54,7-76) years and 53.6% of patients were aged [&ge;] 65 years. During the observational period of 68 (IQR, 66-70) days, AEs occurred in 57.9% and 61.9% of patients after the first dose and second dose, respectively. The most common AEs were: injection-site pain (61.9%), erythema (4.8%), itching (4.8%), swelling (16.7%), axillary swelling/tenderness (2.4%), fever (17.5%) headache (7.9%), fatigue (23.8%), myalgia (17.5%), arthralgia (12.7%), dyspnoea (2.4%), nausea/vomiting (7.1%), diarrhoea (5.6%), shivers (4%) and vertigo (1.6%). The rates of local AEs were similar after the first and second doses (P=0.8), whereas systemic AEs occurred more frequently after the second dose (P=0.001). Fever (P=0.03), fatigue (P=0.02) and nausea/vomiting (P=0.03) were significantly more frequent after the second dose of the vaccine. There were no age-related differences in the rate of AEs. Overall, vaccine-related AEs in hemodialysis patients seem to be lower than in the general population. ConclusionRNA-1273 vaccine was associated with the development of transient AEs after the first (57.9%) and second doses (61.9%) in patients on chronic maintenance hemodialysis. Systemic AEs were more common after the second dose. Overall, all AEs lasted for a few days, without any apparent sequelae.