COVID

ObjectivesThis note provides an assessment of COVID-19 acceleration among groups with different vaccine status in France. MethodsWe assess viral acceleration using a novel indicator introduced in Baunez et al. (2021). The acceleration index relates the percentage change of tests that have been performed on a given day to the percentage change in the associated positive cases that same day. We compare viral acceleration among vaccinated and unvaccinated individuals in France over the period May 31st - August 29, 2021. ResultsOnce the state of the epidemic within each groups is accounted for, it turns out that viral acceleration has since mid-July converged to similar levels among vaccinated and unvaccinated individuals in France, even though viral speed is larger for the latter group compared to the former. ConclusionOur results call for an increasing testing effort for both vaccinated and unvaccinated individuals, in view of the fact that viral circulation is currently accelerating at similar levels for both groups in France.

BackgroundBreakthrough events are not rare after emerging of Delta variant. On the other hand, long COVID is an unsolved issue where sufferers suffer a lot. Some study has shown that COVID-19 vaccine has improved some clinical and libratory parameters in long COVID. But what will be the possible measures against long COVID after the breakthrough event is still a burning question. MethodWe have observed the third dose by BNT162b2 in a small group(n=20) who were diagnosed as long COVID after breakthrough infections, in Sheikh Hasina National Institute of Burn & Plastic Surgery Institute, Dhaka, Bangladesh. CRP(C-reactive protein) and Anti S1 RBD IgG responses were measured. ResultAll 20 participants in the study received both dosage of "ChAdOx1-nCoV-19" in between February 2021 to April 2021 and had breakthrough infection in the same or following month which led to long COVID syndrome. They all received a third dose of "BNT162b2". A before and after 3rd dose (14 days after) CRP from participants serum was measured. A Wilcoxon matched paired signed rank test revealed significant (P value <0.05) reduction of inflammatory marker (CRP) after receiving the 3rd vaccine dose. Pre and post 3rd dose quantitative anti S1-RBD IgG response was measured and compared that revealed significant boosting effect that clearly correlates with the CRP response. ConclusionCoverage of vaccines all over the world is still not expected level to control this pandemic. WHO has not recommended the use of a third/booster dose of COVID vaccines. Though our results show some sort of hope for the long COVID in breakthrough events after getting the third dose more study is needed to conclude this issue.

COVID-19 vaccination resistance has become a major challenge to prevent global SARS-CoV-2 transmission. Here we report that the vaccination coverage rate is inversely correlated to the mutation frequency of the full genome (R2=0.878) and spike gene (R2=0.829) of SARS-CoV-2 delta variants in 16 countries, suggesting that full vaccination against COVID-19, with other mitigation strategies, is critical to suppress emergent mutations. Neutrality analysis of DH and Zengs E tests suggested that directional selection was the major driving force of delta variant evolution. To eliminate the homogenous effects (population expansion, selective sweep etc.), the synonymous (Dsyn) and nonsynonymous (Dnonsyn) polymorphisms of the delta variant spike gene were estimated with Tajimas D statistic. Both D ratio (Dnonsyn/Dsyn) and {Delta}D (Dsyn-Dnonsyn) have positive correlation with the full vaccination rate (R2= 0.723 and 0.505, respectively) in 19 countries, indicating that purifying selection pressure of SARS-CoV-2 spike gene increased as the vaccination coverage rate increased. Taken together, our data suggests that vaccination plays an important role in the purifying selection force of spike protein of SARS-CoV-2 delta variants.

Infection born by Coronavirus SARS-CoV-2 has swept the world within a time of a few months. It has created a devastating effect on humanity with social and economic depression. Europe and America were the hardest hit continents. India has also lost lives, making the country fourth most deadly worldwide. However, the infection and death rate per million and the case fatality ratio in India were substantially lower than in many developed nations. Several factors have been proposed including genetics. One of the important facts is that a large chunk of Indian population is asymptomatic to the SARS-CoV-2 infection. Thus, the real infection in India is much higher than the reported number of cases. Therefore, the majority of people are already immune in the country. To understand the dynamics of real infection as well as the level of immunity against SARS-CoV-2, we have performed antibody testing (serosurveillance) in the urban region of fourteen Indian districts encompassing six states. In our survey, the seroprevalence frequency varied between 0.01-0.48, suggesting high variability of viral transmission between states. We also found out that the cases reported by the government were several fold lower than the real incidence of infection. This discrepancy is mainly driven by the higher number of asymptomatic cases. Overall, we suggest that with the high level of immunity developed against SARS-CoV-2 in the majority of the districts, the case fatality rate of second wave in India will be minor than first wave.

The Omicron variant of coronavirus has caused major disruptions world-wide with countries struggling to manage the overwhelming number of infections. Omicron is found to be significantly more transmissible compared to its predecessors and therefore almost every impacted country is exhibiting new infection peaks than seen earlier. In this work, we analyze the global statistics of Omicron-impacted countries including South Africa, the United Kingdom, the United States, France, and Italy to quantitatively estimate the intensity and severity of recent waves. Next, these statistics are used to estimate the impact of Omicron in India, which is experiencing an intense third wave of COVID-19 since 28 Dec., 2021. The rapid surge in the daily number of infections, comparable to the global trends, strongly suggests the dominance of the Omicron variant in infections in India. The logarithmic regression suggests the early growth rate of infections in this wave is nearly four times that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks all across the country; the effective reproduction number (Rt) although has significant variations among different regions. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the Susceptible-Infected-Removed (SIR) model suggest that the peak in India to occur in late January 2022 with a caseload exceeding that in the second wave. Although global Omicron trends, as analyzed in this work, suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden accumulation of active infections can potentially choke the already stressed healthcare infrastructure for the next few weeks.

Comprehending how individuals feel when they discuss the vaccine is important for the immunization campaign and outbreak management during a health emergency. Online conversations provide useful information for assessing sentimental and emotional reactions to the evolutions of the pandemic and immunization program. In this study, we employ a corpus of around 58 million English tweets from users in 17 countries that discuss vaccine-related topics in the year 2021. We apply Soft Dynamic Time Warping algorithm and Time Lag Cross-Correlation approach and find that the evolutions of sentiments closely mirror the pandemic statistics. We also examine five topics connected to vaccination and discover that trust is the most predominate feeling, followed by fear, anger, and joy. Some countries reported higher emotional scores on a theme than others (people in Cuba and the United States exhibit higher levels of trust, Pakistanis and Indians express higher levels of joy, Australians and Chinese express higher levels of fear, and Japanese and British people express higher levels of anger). This study report offers a viewpoint on the publics response to the epidemic and vaccination and aids policy-makers with preventive strategies for a future crisis.

BackgroundA fully quantitative picture of national effectiveness in controlling the spread of the Covid19 virus should consider the percentage of a population vaccinated in relation to the percentage of a population as active cases. MethodsPublicly available data from 27 European countries on nine dates in 2021. Data were (i) initial Covid19 vaccinations and (ii) Covid19 active cases, both as percentages of a countrys population. Dividing (i) by (ii) yielded a new metric, the V ratio, which can increase as (i) increase or as (ii) decreases or both. I correlated the change in V ratio with the change in R statistic in the 27 counties and nine dates. ResultsMean European V ratio increased from January 11 2021 onwards; inverse correlation was found between V ratio and R statistic (p<0.001, r2=0.15, df=234). Initial threshold V ratio of 10-15 resulted in an R statistic of 1.0 or lower; this threshold increased to 30-40 with further vaccinations. Variation between countries in the V ratio increased with time. ConclusionThis quantitative assessment and use of a summary data-derived threshold index showed the integrated effectiveness of vaccinations and social measures for European countries for Covid19. It established a threshold range for an R value of 1 and calculation of the number of vaccinations needed in Europe to reduce the infectivity of the virus to unity. Results can be used to quantify the relation between transmission following vaccination and social measures to control the spread of Covid19. Summary box What is already known in the epidemiology of the Covid19 pandemic in Europe countries is time- and country-based estimates of the R statistic as a measure of infectivity, the percentages of vaccinated persons to reduce infectivity and the number of active cases amenable to social measures. What is not known is how these three parameters interact. What does this study add? This study adds by invention an index (V) of percentage vaccinated population divided by percentage active cases. It then examines the corresponding V index in relation to the R statistic. It derives a range threshold V ratio for an R statistic of unity and extends this to suggest the total number of vaccines needed in Europe. Policy implicationsThese results will allow policy judgements to be made on the basis of measured evidence, and not just models, of the means to reduce the Covid19 pandemic in Europe. The R statistic captures the development of the pandemic; the V ratio measures the integrated and quantified measures to reduce it. Further development of the analysis could assist in calculating the relative effectiveness of vaccination and social measures linked to a range of values of the V ratio. It can also be used as an alarm call to identify states which, even given 100% vaccination, may not reduce their R statistic below unity.

At the begging of 2020 saw the development and trials of vaccines against Covid-19 at an unprecedented pace. The first half of 2021 has seen vaccine rollout in many countries, on the other hand, Immunity to covid-19 has exhibited to minimize the risk of having a severe infection and initiate an excellent degree against the disease. This study focuses on the comparison of Anti-Spike IgG antibodies among vaccinated people with or without previous exposure to the coronavirus. To determine whether a single dose of sputnik V can produce significant antibody titer amongst previously infected cases and design vaccine dosage regimens accordingly. This study was performed at Libyan biotechnology research Centre from August 2021 to December 2021. Blood samples were collected from 1811 adult males and females vaccinated with and without a history of exposure to covid-19. Previously infected individuals record was noted separately. Samples were immediately analyzed by Beckman Unicel Dxl 600, Access immunoassay system. Data were analyzed using GraphPad Prism 9 Software. A P-value >0.5 was not significant. The Majority of candidates 60% of the total samples were males and on analysis, it was found that 72% of patients were seropositive, on the other hand, individuals who vaccinated and have naive antibodies from the previous infection showed slightly higher immunological response rather than vaccinated patients without previous infection and this finding can help the policymakers to design a single-dose vaccine regimen for the former category.

With the development of high-efficacy vaccines against SARS-CoV-2, an urgent open question is whether currently available vaccines protect with similar efficacy against infection with SARS-CoV-2 variants of concern (VOC). Recent reports quantifying the extent by which VOC can evade vaccine immunity resulted in a range of estimates for the same VOC, which makes them difficult to interpret. One possible explanation for the discrepancies between different studies is an inconsistency in terms of the time post-vaccination of the sampled population. Here we present a model based on the observed correlation between antibody neutralization levels and vaccine efficacy, which demonstrates the impact of time post-vaccination on the comparison of the vaccine efficacy for VOC versus non-VOC infections. Our model predicts and exemplifies several possible consequences for vaccine efficacy in VOC infections: 1) a delay in the onset of vaccine efficacy against VOC; 2) a transient increase in susceptibility to breakthrough infection with VOC compared to non-VOC as a function of time after vaccination. We review preliminary data indicating that such phenomena are observed in studies of the B.1.1.7 and B.1.351 variants. We find that ignoring the strong dependence on the time post-vaccination can lead to contradictory reports of relative efficacy against VOC versus non-VOC, with implications on mitigation strategies against VOC and the design of vaccine efficacy studies.

BackgroundTB latent infection reflected as TB prevalence might give heterogeneous immunity for infection in mechanism like what happen in BCG This study the first to our knowledge, addressing TB prevalence influence and possibly an important predictor for Covid-19 mortality and its findings may help to satisfy world inquiries about diversities dilemma. This study also will address disparities raised before about variances in mortalities among countries with same BCG protocols. MethodsThis study was set to look out for impact of TB prevalence on Covid-19 mortality on the context of countries vaccination status. Countries were divided into five groups according to BCG status. Covid-19 deaths are tested against TB prevalence through using (Non Linear Regression Modules) of predicted shapes behavior for each group. ResultsSlopes values have highly significant influences between TB prevalence and Covid-19 deaths for overall studied group, and among countries currently given 1 BGG (2 groups) and ones with previous history of vaccinations, being significant in currently given more than 1 BCG and in countries without vaccination. There are meaningful nonlinear regression shapes which are logarithmic in whole countries and in countries with current just 1 vaccine setting. It is inverse in other 2 groups currently given vaccine. It is power and cubic in countries never given and with previously given vaccines respectively. All groups and whole sample shows either perfect or extremely perfect R-square (Determination Coefficient) values with significant in at least at P-values<0.05. Study denotes possibility of factor/s other than BCG prevalence (i.e. The intercept) were operating in different ranges within groups. ConclusionHigh TB prevalence together with continuing BCG programs decrease COVID-19 Mortalities in different countries. Strengths and limitations of this studyO_LITo our knowledge, this study will be the first addressing TB Prevalence influence and possibly an important predictor for Covid-19 mortality and its findings may help to satisfy world inquiries about diversities dilemma. C_LIO_LIThis study also will address disparities raised before about variances in mortalities among countries with same BCG protocols. C_LIO_LIPotential confounding factors still exist. C_LI

AimsWe compared the number of non-COVID-19 deaths between April 2020 and June 2022 to the expected number of deaths based on the patterns observed in the five years prior to the pandemic in France with the aims of (a) estimating the reduction in non-COVID-19 mortality, particularly due to reduction in the circulation of other respiratory viruses during the pandemic; (b) examining the degree to which SARS-CoV-2 infection was detected and characterized as a cause of death during different periods of the pandemic. MethodsUsing a previously developed regression model, we expressed weekly mortality rates in the 5-year period prior to the pandemic as a combination of influenza-associated mortality rates and baseline and a linear trend for the rates of non-influenza mortality. Estimates for the baseline and trend for non-influenza mortality together with estimates of influenza-related mortality prior to the pandemic were used to estimate expected mortality during the pandemic period. ResultsThe number of recorded non-COVID-19 deaths between week 15, 2020 and week 26, 2022 in France was less than the expected number of deaths by 49,623 (95% CI (20364,78837)). Additionally, rates of non-COVID-19 mortality increased during the later part of the study period, with the difference between the number of non-COVID-19 deaths and the expected number of deaths during the last 52 weeks of the study period being greater than the corresponding difference for the first 52 weeks of the study period by 28,954 (24979,32918) deaths. ConclusionsOur results suggest (a) the effectiveness of mitigation measures during the pandemic for reducing the rates of non-COVID-19 mortality, particularly mortality related to circulation of other respiratory viruses, including influenza (that was responsible for an annual average of 15,334 (12593,18077) deaths between 2015-2019 in France); (b) detection of a high proportion of SARS-CoV-2 infections leading to deaths in France, and characterization of those infections as the underlying cause of death. Additionally, while the increase in non-COVID-19 mortality during the later part of the study period is partly related to the temporal increase in the circulation of other respiratory viruses, there was an increase, particularly during the period of the circulation of the Omicron variant, in the proportion of hospitalizations with a SARS-CoV-2 infection in France that were coded as hospitalizations with COVID-19 (rather than COVID-19 hospitalizations), suggesting an increasing proportion of SARS-COV-2-associated deaths not being coded as COVID-19 deaths. All of this suggests the importance of timely detection of infections with SARS-CoV-2, particularly the Omicron variant (for which manifestations of disease complications are different compared to the earlier variants), and of providing the necessary treatment to patients to avoid progression to fatal outcomes.

By the end of February 2021, when 48% of the Israeli population was immune, the number of new positive COVID-19 cases significantly dropped across all ages. Understanding which parameters influenced this drop and how to minimize the number of hospitalizations and overall positive cases is urgently needed. In this study we conducted an observational analysis which included COVID-19 data with over 12,000,000 PCR tests from 250 cities in Israel. In addition, we performed a simulation of different vaccination campaigns to find the optimal policy. Our analysis revealed that cities with younger populations reached a decrease in new cases when a lower percentage of their residents were immunized, showing that median age is a crucial parameter effecting overall immunity, while other parameters appeared to be insignificant. This variance between cities is explained by recalculating the immunized population and multiplying each individual by a factor symbolizing the impact of their age on the spread on the virus. This factor is easily calculated from historical data of positive cases per age. The simulation proves that prioritizing different age groups or changing the rate of vaccinations drastically effects the overall hospitalizations and positive cases. One-Sentence Summaryunderstanding what influences reaching covid-19 overall immunity and how to maximize the effect of the vaccination campaign.

IntroductionIn November 2020, a new SARS-COV-2 variant or the Kent variant emerged in the UK, and became the dominant UK SARS-COV-2 variant, demonstrating faster transmission than the original variant, which rapidly died out. However, it is unknown if this altered the overall course of the pandemic as genomic analysis was not common place at the outset and other factors such as the climate could alter the viral transmission rate over time. We aimed to test the hypothesis that the overall observed viral transmission was not altered by the emergence of the new variant, by testing a model generated earlier in the pandemic based on lockdown stringency, temperature and humidity. MethodsFrom 1/1/20 to 4/2/21, the daily incidence of SARS-COV-2 deaths and the overall stringency of National Lockdown policy on each day was extracted from the Oxford University Government response tracker. The daily average temperature and humidity for London was extracted from Wunderground.com. The viral reproductive rate was calculated on a daily basis from the daily mortality data for each day. The correlation between log10 of viral reproductive rate and lockdown stringency and weather parameters were compared by Pearson correlation to determine the time lag associated with the greatest correlation. A multivariate model for the log10 of viral reproductive rate was constructed using lockdown stringency, temperature and humidity for the period 1/1/20 to 30/9/20. This model was extrapolated forward from 1/10/20 to 4/2/21 and the predicted viral reproductive rate, daily mortality and cumulative mortality were compared with official data. ResultsOn multivariate linear regression, the optimal model had and R2 0f 0.833 for prediction of log10 viral reproductive rate 13 days later in the model construction period, with (coefficient, probability) lockdown stringency (-0.0109, p=0.0000), humidity (0.0038, p=0.0041) and temperature (-0.0035, p=0.0008). When extrapolated to the validation period (1/10/20 to 4/2/21), the model was highly correlated with daily (Pearson coefficient 0.88, p=0.0000) and cumulated SARS-COV-2 mortality (Pearson coefficient 0.99, p=0.0000). ConclusionThe course of the SARS-COV-2 pandemic in the UK seems highly predicted by an earlier model based on the lockdown stringency, humidity and temperature and unaltered by the emergence of a newer viral genotype.

IntroductionEarly after the start of the COVID-19 pandemic, a major drop in the number of influenza B/Yamagata detections was observed globally. Given the potential public health implications, particularly with regards to influenza vaccination, we conducted a systematic review of influenza B/Yamagata virus circulation data from multiple complementary sources of information. MethodsWe searched articles published until 20th March 2023 in PubMed and EMBASE; examined epidemiological and virological influenza data for 2020-2023 contained in the publicly available WHO-FluNet and GISAID (Global Initiative on Sharing All Influenza Data) global databases, or collected by the multi-national Global Influenza Hospital Surveillance Network (GIHSN) study; and looked for influenza data in the webpages of respiratory viruses surveillance systems from countries worldwide. ResultsHighly consistent findings were found across all sources of information, with a progressive decline of influenza B/Yamagata detections from 2020 onwards across all world regions, both in absolute terms (total number of cases), the positivity rate, and as a fraction of influenza B detections. Isolated influenza B/Yamagata cases continue to be sporadically reported, and these are typically vaccine-derived, mistaken data entries or under investigation. DiscussionWhile it is still too early to conclude that B/Yamagata is (or will soon become) extinct, the current epidemiological and virological data call for a rapid response in terms of influenza prevention practices, particularly regarding the formulation of influenza vaccines. The current epidemiological situation is unprecedented in recent decades, underlying the importance of continuously and carefully monitoring the circulation of influenza viruses (as well as SARS-CoV-2 and the other respiratory viruses) in the coming years.

This is the first study on omicron genomes from India to focus on phylodynamics and phylogenomics trait to provide an insight into the evolution of omicron variants. We analyzed 564 genomes deposited to GISAID database from various states of India. Pangolin COVID-19 Lineage Assigner tool was used to determine lineage assignment of all retrieved genomes. A Maximum likelihood (MLE) tree construction further confirms the separation of genomes into two distinct clades, BA. 1. and BA. 2. A very high reproduction number (R0) of 2.445 was estimated for the lineage BA.2. The highest R0 value in Telangana confirms the prevalence of lineage BA.2 in the state. Construction of the Reduced Median (RM) network shows evolution of some autochthonous haplogroups and haplotypes, which further supports the rapid evolution of omicron as compared to its previous variants. Phylogenomic analyses using maximum likelihood (ML) and RM show the potential for the emergence of sub-sublineages and novel haplogroups respectively. Due to the recombinant property and high transmissibility of omicron virus, we suggest continuous and more widespread genome sequencing in all states of India to track evolution of SARS-CoV-2 in real time.

BackgroundIn 2021 a new variant of SARS-CoV-2, which came to be called the alpha variant, spread around the world. There were conflicting reports on this COVID-19 variant strains potentially increased lethality. In Israel, this strain became predominant in a very short time period. MethodsCOVID-19 mortality and case fatality rates were examined in Israel in terms of weekly and cumulative numbers. ResultsCOVID-19 case fatality rates in Israel rose quickly at the beginning of the pandemic and peaked in May 2020. The highest crude mortality came later in the second and third waves, but case the case fatality rates did not rise in 2021 with the increasing dominance of the alpha variant. ConclusionsBased on the results of examining case-fatality and mortality rates, we concluded that while the alpha variant of the virus raised mortality, in line with the fact that it is more infectious than wild-type, once this strain was caught by patients in Israel, it was not more likely to kill them than the original strain

BackgroundFull immunization with two doses of Covid vaccine has been found to be a critical factor in preventing morbidity and mortality from the Covid-19 infection. However, due to the shortage of vaccines, a significant portion of the population is not getting vaccination in many countries. Also, the distribution of vaccine doses between prospective first dose recipient and second dose recipient is not uniformly planned, as seen in Indias various states and union territories. It is recommended to give second vaccine doses within 4-8 weeks to first dose recipients for both the approved vaccines in India; hence the judicious distribution between non-immunized and partly immunized populations is essential. Managing the Covid-19 vaccination drive in an area with a large number of single-dose recipients compared to a smaller number of fully immunized people can become a huge administrative challenge. Therefore, this study was conducted to assess the number of people covered under the Covid vaccination drive in India and analyze the state-wise distribution of vaccines among the non-immunized and partly immunized population. MethodsThe Covid 19 vaccination data till 7th may, 2021 was taken from the website of the Ministry of Health and Family Welfare, Govt of India. From the data available of the number of doses injected, other figures like the total number of people vaccinated, people with two doses of vaccine or full immunization (FI), and those with a single dose of vaccine or partial immunization (PI) were found. The percentage of the fully immunized and partly immunized population was also found. A ratio between fully immunized and partly immunized individuals (FI: PI) was proposed as a guide to monitor the progress of the vaccination and future dose distribution of two-dose Covid-19 vaccines among partly immunized (PI) and non-immunized (NI) population. ResultsIn India, till 7 May 2021, 16,49,73,058 doses of Covid-19 vaccines have been injected. A total of 13,20,87,824 people received these vaccine doses, with 9,92,02,590 people getting a single dose or were partly immunized (PI), and 3,28,85,234 got two doses each or were fully immunized (FI). Among the states, Tripura and Andhra Pradesh had the highest FI: PI (Fully Immunized: Partly Immunized) ratio of 0.86 and 0.52, followed by Tamil Nadu, Arunachal Pradesh, and West Bengal with figures of 0.48. 0.47 and 0.47, respectively. Telangana and Punjab had the lowest FI: PI ratio among the states at 0.2 each, with Chhattisgarh, Madhya Pradesh, and Haryana following at 0.21. 0.23 and 0.23, respectively. These values are much lower than the national average of 0.33 in India. ConclusionThe FI: PI ratio could help governments decide how to use scarce vaccine resources among first-time and second-time recipients. This simple mathematical tool could ensure full immunization status to maximum people within the recommended 4-8 week time window after the first dose to avoid a large population group with partly immunized status.

The B.1.1.7 strain, also referred to as Alpha variant, is a variant strain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Alpha variant is considered to possess higher transmissibility compared to the strains previously circulating in England. This paper proposes a new method to estimate the selective advantage of a mutant strain over another strain using the time course of strain frequencies and the distribution of the serial interval of infections. This method allows the instantaneous reproduction numbers of infections to vary over calendar time. The proposed method also assumes that the selective advantage of a mutant strain over previously circulating strains is constant. Applying the method to SARS-CoV-2 sequence data from England, the instantaneous reproduction number of the B.1.1.7 strain was estimated to be 26.6-45.9% higher than previously circulating strains in England. This result indicates that control measures should be strengthened by 26.6-45.9% when the B.1.1.7 strain is newly introduced to a country where viruses with similar transmissibility to the preexisting strain in England are predominant.

BackgroundBefore the availability of vaccines, countries largely relied on mobility restriction and testing to mitigate the COVID-19 pandemic. Our aim is to assess the combined impact of mobility restriction, testing, and vaccination on the COVID-19 pandemic in India. MethodsWe conducted a multiple regression analysis to assess the impact of mobility, testing, and vaccination on COVID-19 incidence between April 28, 2021 to November 24, 2021 using data from Our World in Data and Google Mobility Report. The 7-day moving average was applied to offset the daily fluctuations in the mobility and testing. Each independent variable was lagged to construct a temporal relationship, and waning vaccination efficacy was taken into consideration. We performed additional analysis for three time periods between March 28, 2020 to November 24, 2021 (1st: March 28, 2020 [~] October 7, 2020, 2nd: October 8, 2020 [~] April 27, 2021, 3rd: April 28, 2021 [~] November 24, 2021) to examine potential heterogeneity over time. ResultsMobility (0.041, 95% CI: 0.033 to 0.048), testing (-0.008, 95% CI: -0.015 to -0.001), and vaccination (quadratic term: 0.004, 95% CI: 0.003 to 0.005, linear term: -0.130, 95% CI: -0.161 to -0.099) were all associated with COVID-19 incidence. For vaccination rate, the decrease of number of cases demonstrated a U-shaped curve, while mobility showed a positive association and testing showed an inverse association with COVID-19 incidence. Mobility restriction was effective during all three periods - March 28, 2020 to November 24, 2021 (0.009, 0.048, and 0.026 respectively). Testing was effective during the second and third period - October 8, 2020 to November 24, 2021 (-0.036, and -0.006 respectively). ConclusionMobility restriction and testing were effective even in the presence of vaccination. This shows the positive value of mobility restrictions, testing, and vaccination from the health system perspective on COVID-19 prevention and control, especially with continual emergence of variants in India and globally. At the same time, this health system gain must be balanced with the challenges in the delivery of non-COVID health services and broader socio-economic impact in deciding the prolonged continuance of mobility restriction.

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.