Expansion and transmission dynamics of high risk carbapenem-resistant Klebsiella pneumoniae subclones in China: An epidemiological, spatial, genomic analysis.

AIMS: Carbapenem-resistant Klebsiella pneumonia (CRKP) is a global threat that varies by region. The global distribution, evolution, and clinical implications of the ST11 CRKP clone remain obscure. METHODS: We conducted a multicenter molecular epidemiological survey using isolates obtained from 28 provinces and municipalities across China between 2011 and 2021. We integrated sequences from public databases and performed genetic epidemiology analysis of ST11 CRKP. RESULTS: Among ST11 CRKP, KL64 serotypes exhibited considerable expansion, increasing from 1.54% to 46.08% between 2011 and 2021. Combining our data with public databases, the phylogenetic and phylogeography analyses indicated that ST11 CRKP appeared in the Americas in 1996 and spread worldwide, with key clones progressing from China's southeastern coast to the inland by 2010. Global phylogenetic analysis showed that ST11 KL64 CRKP has evolved to a virulent, resistant clade with notable regional spread. Single-nucleotide polymorphism (SNP) analysis identified BMPPS (bmr3, mltC, pyrB, ppsC, and sdaC) as a key marker for this clade. The BMPPS SNP clade is associated with high mortality and has strong anti-phagocytic and competitive traits in vitro. CONCLUSIONS: The high-risk ST11 KL64 CRKP subclone showed strong expansion potential and survival advantages, probably owing to genetic factors.

Sources of Human Immunodeficiency Virus Infections Among Men Who Have Sex With Men With a Migration Background: A Viral Phylogenetic Case Study in Amsterdam, The Netherlands.

BACKGROUND: Men and women with a migration background comprise an increasing proportion of incident human immunodeficiency virus (HIV) cases across Western Europe. METHODS: To characterize sources of transmission in local transmission chains, we used partial HIV consensus sequences with linked demographic and clinical data from the opt-out AIDS Therapy Evaluation in the Netherlands (ATHENA) cohort of people with HIV in the Netherlands and identified phylogenetically and epidemiologically possible HIV transmission pairs in Amsterdam. We interpreted these in the context of estimated infection dates, and quantified population-level sources of transmission to foreign-born and Dutch-born Amsterdam men who have sex with men (MSM) within Amsterdam transmission chains. RESULTS: We estimate that Dutch-born MSM were the predominant sources of infections among all Amsterdam MSM who acquired their infection locally in 2010-2021, and among almost all foreign-born Amsterdam MSM subpopulations. Stratifying by 2-year intervals indicated time trends in transmission dynamics, with a majority of infections originating from foreign-born MSM since 2016, although uncertainty ranges remained wide. CONCLUSIONS: Native-born MSM have predominantly driven HIV transmissions in Amsterdam in 2010-2021. However, in the context of rapidly declining incidence in Amsterdam, the contribution from foreign-born MSM living in Amsterdam is increasing, with some evidence that most local transmissions have been from foreign-born Amsterdam MSM since 2016.

Network-based strategies to combat HCV: Examining social and spatial drivers of transmission among PWID in New Delhi.

People who inject drugs (PWID) account for some of the fastest-growing HCV epidemics globally. While individual risk factors for infection are understood, less is known about network and spatial factors critical for elimination strategies. Two thousand five hundred twelve PWID in New Delhi, India, were recruited (2017-19) through network referrals. Biometrics identified duplicates and cross-network linkages. Participants completed semi-annual surveys and blood tests for HCV antibodies and RNA. Poisson regression and network analyses identified predictors of incident HCV and compared network-based intervention approaches. Baseline HCV antibody prevalence was 65.1%, of whom 79.6% were HCV RNA-positive. We observed 92 HCV seroconversions over 382.25 person-years (incidence: 24.1 per 100 person-years). Of the 92 seroconverters, 67% (62) were directly connected to an RNA-positive participant, and all were within one degree of separation from an RNA-positive participant. Individual-level factors associated with seroconversion included age, sexual activity, and injection behaviours. After adjusting for individual-level factors, seroconversion was significantly associated with number of RNA-positive partners (adjusted incidence rate ratio [AIRR] = 1.30) and injecting at a particular venue (AIRR = 2.53). This association extended to indirect ties, with 17% reduced odds of seroconversion for each degree of separation from the venue (AIRR = 0.83). Network analyses comparing intervention strategies found that targeting venues identified more cases compared to a treat-a-friend approach. We observed a fast-growing HCV epidemic driven by viremia within individuals' immediate networks and indirect social and spatial ties, demonstrating the importance of achieving broad, sustained virologic response and rethinking network-based interventions to include venues.

In-silico insights of ESBL variants and tracking the probable sources of ESBL-producing Escherichia coli in a small-scale poultry farm.

Commercial broiler farms face challenges of extended spectrum beta-lactamase (ESBL)-producing Escherichia coli transmitted from both vertical and horizontal routes. Understanding the dynamics of ESBL-E. coli transmission in compromised biosecurity settings of small-scale rural poultry farms is essential. This study aimed to elucidate the probable transmission pathways of ESBL-E. coli in such settings, employing phylogenetic analysis and molecular docking simulations to explore the catalytic properties of ß-lactamase variants. Sampling was conducted on a small-scale poultry farm in West Bengal, India, collecting 120 samples at three intervals during the broiler production cycle. E. coli isolates underwent resistance testing against eight antimicrobials, with confirmation of ESBL production. Genotypic analysis of ESBL genes and sequencing were performed, alongside molecular docking analyses and phylogenetic comparisons with publicly available sequences. Among 173 E. coli isolates, varying resistance profiles were observed, with complete resistance to cefixime and high resistance to amoxicillin and tetracycline. The incidence of ESBL-E. coli fluctuated over the production cycle, with dynamic changes in the prevalence of blaCTX-M-type and blaSHV-type genes. Phylogenetic analysis indicated partial clonal relationships with human clinical strains and poultry strains from the Indian subcontinent. Molecular docking confirmed the catalytic efficiencies of these ESBL variants. The study highlights probable vertical transmission of ESBL-E. coli and emphasizes drinking water as a potential source of horizontal transmission in small-scale poultry farms. Strict biosecurity measures could prevent the spread of antimicrobial-resistant bacteria in birds and their products in a small scale poultry farm.

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.

PURPOSE OF REVIEW: Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. RECENT FINDINGS: Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.

Revisiting the Modularity-Disease transmission Link: Uncovering the importance of intra-modular structure.

Studies have shown that the internal structure of modules is hardly important for the spread of epidemics. However, most of these studies have assumed that intra-module connectivity and inter-module connectivity do not affect each other. In reality, changes in the internal structure of modules may affect inter-module links and thus change the modularity of the entire network. Therefore, we have developed a theoretical network model with adjustable modularity to investigate the impact of this situation on disease transmission. Our findings indicate that the intra-module structure plays a crucial role in disease outbreaks. Changes in intra-module structure lead to significant numerical changes in peak prevalence and duration of disease. This implies that the potential impact of changes in exposure patterns within modules should also be considered when investigating the exact impact of modular social networks on disease burden.

Quantifying and Predicting Ongoing Human Immunodeficiency Virus Type 1 Transmission Dynamics in Switzerland Using a Distance-Based Clustering Approach.

BACKGROUND: Despite effective prevention approaches, ongoing human immunodeficiency virus 1 (HIV-1) transmission remains a public health concern indicating a need for identifying its drivers. METHODS: We combined a network-based clustering method using evolutionary distances between viral sequences with statistical learning approaches to investigate the dynamics of HIV transmission in the Swiss HIV Cohort Study and to predict the drivers of ongoing transmission. RESULTS: We found that only a minority of clusters and patients acquired links to new infections between 2007 and 2020. While the growth of clusters and the probability of individual patients acquiring new links in the transmission network was associated with epidemiological, behavioral, and virological predictors, the strength of these associations decreased substantially when adjusting for network characteristics. Thus, these network characteristics can capture major heterogeneities beyond classical epidemiological parameters. When modeling the probability of a newly diagnosed patient being linked with future infections, we found that the best predictive performance (median area under the curve receiver operating characteristic AUCROC = 0.77) was achieved by models including characteristics of the network as predictors and that models excluding them performed substantially worse (median AUCROC = 0.54). CONCLUSIONS: These results highlight the utility of molecular epidemiology-based network approaches for analyzing and predicting ongoing HIV transmission dynamics. This approach may serve for real-time prospective assessment of HIV transmission.

Sequential viral introductions and spread of BA.1 across Pakistan provinces during the Omicron wave.

BACKGROUND: COVID-19 waves caused by specific SARS-CoV-2 variants have occurred globally at different times. We focused on Omicron variants to understand the genomic diversity and phylogenetic relatedness of SARS-CoV-2 strains in various regions of Pakistan. METHODS: We studied 276,525 COVID-19 cases and 1,031 genomes sequenced from December 2021 to August 2022. Sequences were analyzed and visualized using phylogenetic trees. RESULTS: The highest case numbers and deaths were recorded in Sindh and Punjab, the most populous provinces in Pakistan. Omicron variants comprised 93% of all genomes, with BA.2 (32.6%) and BA.5 (38.4%) predominating. The first Omicron wave was associated with the sequential identification of BA.1 in Sindh, then Islamabad Capital Territory, Punjab, Khyber Pakhtunkhwa (KP), Azad Jammu Kashmir (AJK), Gilgit-Baltistan (GB) and Balochistan. Phylogenetic analysis revealed Sindh to be the source of BA.1 and BA.2 introductions into Punjab and Balochistan during early 2022. BA.4 was first introduced in AJK and BA.5 in Punjab. Most recent common ancestor (MRCA) analysis revealed relatedness between the earliest BA.1 genome from Sindh with Balochistan, AJK, Punjab and ICT, and that of first BA.1 from Punjab with strains from KPK and GB. CONCLUSIONS: Phylogenetic analysis provides insights into the introduction and transmission dynamics of the Omicron variant in Pakistan, identifying Sindh as a hotspot for viral dissemination. Such data linked with public health efforts can help limit surges of new infections.

Molecular epidemiology and transmission dynamics of multi-drug resistant tuberculosis strains using whole genome sequencing in the Amhara region, Ethiopia.

BACKGROUND: Drug resistant Mycobacterium tuberculosis prevention and care is a major challenge in Ethiopia. The World health organization has designated Ethiopia as one of the 30 high burden multi-drug resistant tuberculosis (MDR-TB) countries. There is limited information regarding genetic diversity and transmission dynamics of MDR-TB in Ethiopia. OBJECTIVE: To investigate the molecular epidemiology and transmission dynamics of MDR-TB strains using whole genome sequence (WGS) in the Amhara region. METHODS: Forty-five MDR-TB clinical isolates from Amhara region were collected between 2016 and 2018, and characterized using WGS and 24-loci Mycobacterium Interspersed Repetitive Units Variable Number of Tandem Repeats (MIRU-VNTR) typing. Clusters were defined based on the maximum distance of 12 single nucleotide polymorphisms (SNPs) or alleles as the upper threshold of genomic relatedness. Five or less SNPs or alleles distance or identical 24-loci VNTR typing is denoted as surrogate marker for recent transmission. RESULTS: Forty-one of the 45 isolates were analyzed by WGS and 44% (18/41) of the isolates were distributed into 4 clusters. Of the 41 MDR-TB isolates, 58.5% were classified as lineage 4, 36.5% lineage 3 and 5% lineage 1. Overall, TUR genotype (54%) was the predominant in MDR-TB strains. 41% (17/41) of the isolates were clustered into four WGS groups and the remaining isolates were unique strains. The predominant cluster (Cluster 1) was composed of nine isolates belonging to lineage 4 and of these, four isolates were in the recent transmission links. CONCLUSIONS: Majority of MDR-TB strain cluster and predominance of TUR lineage in the Amhara region give rise to concerns for possible ongoing transmission. Efforts to strengthen TB laboratory to advance diagnosis, intensified active case finding, and expanded contact tracing activities are needed in order to improve rapid diagnosis and initiate early treatment. This would lead to the interruption of the transmission chain and stop the spread of MDR-TB in the Amhara region.

Incidence and Transmission Dynamics of Bordetella pertussis Infection in Rural and Urban Communities, South Africa, 2016‒2018.

We conducted 3 prospective cohort studies (2016-2018), enrolling persons from 2 communities in South Africa. Nasopharyngeal swab specimens were collected twice a week from participants. Factors associated with Bordetella pertussis incidence, episode duration, and household transmission were determined by using Poisson regression, Weibull accelerated time-failure, and logistic regression hierarchical models, respectively. Among 1,684 participants, 118 episodes of infection were detected in 107 participants (incidence 0.21, 95% CI 0.17-0.25 infections/100 person-weeks). Children <5 years of age who had incomplete vaccination were more likely to have pertussis infection. Episode duration was longer for participants who had higher bacterial loads. Transmission was more likely to occur from male index case-patients and persons who had >7 days infection duration. In both communities, there was high incidence of B. pertussis infection and most cases were colonized.

Quantifying Mycobacterium tuberculosis Transmission Dynamics Across Global Settings: A Systematic Analysis.

The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and in which the size distributions of putative TB transmission clusters were enumerated. We fitted cluster-size-distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproduction number) and the dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, 9 studies from 8 global settings met the inclusion criteria (n = 5 studies of all TB; n = 4 studies of drug-resistant TB). Estimated $R$ values (range, 0.10-0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range, 0.02-0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range, 2%-31%) drive the majority (80%) of ongoing TB transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

Transmission dynamics of lyssavirus in Myotis myotis: mechanistic modelling study based on longitudinal seroprevalence data.

We investigated the transmission dynamics of lyssavirus in Myotis myotis and Myotis blythii, using serological, virological, demographic and ecological data collected between 2015 and 2022 from two maternity colonies in northern Italian churches. Despite no lyssavirus detection in 556 bats sampled over 11 events by reverse transcription-polymerase chain reaction (RT-PCR), 36.3% of 837 bats sampled over 27 events showed neutralizing antibodies to European bat lyssavirus 1, with a significant increase in summers. By fitting sets of mechanistic models to seroprevalence data, we investigated factors that influenced lyssavirus transmission within and between years. Five models were selected as a group of final models: in one model, a proportion of exposed bats (median model estimate: 5.8%) became infectious and died while the other exposed bats recovered with immunity without becoming infectious; in the other four models, all exposed bats became infectious and recovered with immunity. The final models supported that the two colonies experienced seasonal outbreaks driven by: (i) immunity loss particularly during hibernation, (ii) density-dependent transmission, and (iii) a high transmission rate after synchronous birthing. These findings highlight the importance of understanding ecological factors, including colony size and synchronous birthing timing, and potential infection heterogeneities to enable more robust assessments of lyssavirus spillover risk.

Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying.

We introduce the angular reproduction number Ω, which measures time-varying changes in epidemic transmissibility resulting from variations in both the effective reproduction number R, and generation time distribution w. Predominant approaches for tracking pathogen spread infer either R or the epidemic growth rate r. However, R is biased by mismatches between the assumed and true w, while r is difficult to interpret in terms of the individual-level branching process underpinning transmission. R and r may also disagree on the relative transmissibility of epidemics or variants (i.e. rA > rB does not imply RA > RB for variants A and B). We find that Ω responds meaningfully to mismatches and time-variations in w while mostly maintaining the interpretability of R. We prove that Ω > 1 implies R > 1 and that Ω agrees with r on the relative transmissibility of pathogens. Estimating Ω is no more difficult than inferring R, uses existing software, and requires no generation time measurements. These advantages come at the expense of selecting one free parameter. We propose Ω as complementary statistic to R and r that improves transmissibility estimates when w is misspecified or time-varying and better reflects the impact of interventions, when those interventions concurrently change R and w or alter the relative risk of co-circulating pathogens.

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China.

BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Resistance of Blastocystis to chlorine and hydrogen peroxide.

Blastocystis is a ubiquitous, widely distributed protist inhabiting the gastrointestinal tract of humans and other animals. The organism is genetically diverse, and so far, at least 28 subtypes (STs) have been identified with ST1-ST9 being the most common in humans. The pathogenicity of Blastocystis is controversial. Several routes of transmission have been proposed including fecal-oral (e.g., zoonotic, anthroponotic) and waterborne. Research on the latter has gained traction in the last few years with the organism having been identified in various bodies of water, tap water, and rainwater collection containers including water that has been previously filtered and/or chlorinated. Herein, we assessed the resistance of 11 strains maintained in culture, spanning ST1-ST9 to various chlorine and hydrogen peroxide concentrations for 24 h, and performed recovery assays along with re-exposure. Following the treatment with both compounds, all subtypes showed increased resistance, and viability could be visualized at the cellular level. These results are hinting at the presence of mechanism of resistance to both chlorine and hydrogen peroxide. As such, this pilot study can be the platform for developing guidelines for water treatment processes.

Microsatellite markers for Opisthorchis felineus to understand its genetic diversity and transmission patterns of opisthorchiosis.

Opisthorchis felineus is a food-borne trematode which causes opisthorchiosis and affects mainly the liver and bile ducts of the liver with a possible risk of bile duct carcinogenesis resulting in cholangiocarcinoma. In Russia, O. felineus is mainly endemic in Western Siberia (Ob and Irtysh river basins) and occurs throughout the Volga, Kama, Don, and Dnepr river basins. The prevalence, intensity, and clinical significance of human infections and the incidence of cholangiocarcinoma vary geographically in endemic regions. Currently, there is substantial evidence on genetic variation of O. felineus, but information on the population genetic structure is so far very scarce. Because microsatellite DNA of this parasite is not available, we for the first time isolated sufficient microsatellite loci to examine the genetic diversity and population structure of O. felineus, using multiple nuclear loci approach. A total of ten highly polymorphic microsatellite loci from a constructed enriched genomic DNA library were characterized, using 29 samples representing huge O. felineus metapopulation extended in latitude over 5000 km from Middle Europe to Western Siberia. At least three populations can be discerned as result of analysis of the microsatellite loci genetic diversity. Based on the results for the first time, a hypothesis was put forward about the formation of a modern habitat of O. felineus.

A Stochastic Mobility-Driven Spatially Explicit SEIQRD covid-19 Model with VOCs, Seasonality, and Vaccines.

In this work, we extend our previously developed compartmental SEIQRD model for sars-cov-2 in Belgium. We introduce sars-cov-2 variants of concern, vaccines, and seasonality in our model, as their addition has proven necessary for modelling sars-cov-2 transmission dynamics during the 2020-2021 covid-19 pandemic in Belgium. The model is geographically stratified into eleven spatial patches (provinces), and a telecommunication dataset provided by Belgium's biggest operator is used to incorporate interprovincial mobility. We calibrate the model using the daily number of hospitalisations in each province and serological data. We find the model adequately describes these data, but the addition of interprovincial mobility was not necessary to obtain an accurate description of the 2020-2021 sars-cov-2 pandemic in Belgium. We further demonstrate how our model can be used to help policymakers decide on the optimal timing of the release of social restrictions.We find that adding spatial heterogeneity by geographically stratifying the model results in more uncertain model projections as compared to an equivalent nation-level model, which has both communicative advantages and disadvantages. We finally discuss the impact of imposing local mobility or social contact restrictions to contain an epidemic in a given province and find that lowering social contact is a more effective strategy than lowering mobility.

Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach.

This paper investigates the dynamic volatility spillover among energy commodities and financial markets in pre-and mid-COVID-19 periods by utilizing a novel TVP-VAR frequency connectedness approach and the QMLE-based realized volatility data. Our findings indicate that the volatility spillover is mainly driven by long-term components and prominently time-varying with a remarkable but short-lived surge during the COVID-19 outbreak. We further spot that WTI and NGS are prevailingly transmitting and being exposed to the system volatility simultaneously, especially during the global pandemic, suggesting the energy commodity market becoming more integrated with, more influential and meanwhile vulnerable to global financial markets.

Universal Community Nucleic Acid Testing for Coronavirus Disease 2019 (COVID-19) in Hong Kong Reveals Insights Into Transmission Dynamics: A Cross-Sectional and Modeling Study.

BACKGROUND: Testing of an entire community has been used as an approach to control coronavirus disease 2019 (COVID-19). In Hong Kong, a universal community testing program (UCTP) was implemented at the fadeout phase of a community epidemic in July to September 2020. We described the utility of the UCTP in finding unrecognized infections and analyzed data from the UCTP and other sources to characterize transmission dynamics. METHODS: We described the characteristics of people participating in the UCTP and compared the clinical and epidemiological characteristics of COVID-19 cases detected by the UCTP versus those detected by clinical diagnosis and public health surveillance (CDPHS). We developed a Bayesian model to estimate the age-specific incidence of infection and the proportion of cases detected by CDPHS. RESULTS: In total, 1.77 million people, 24% of the Hong Kong population, participated in the UCTP from 1 to 14 September 2020. The UCTP identified 32 new infections (1.8 per 100000 samples tested), consisting of 29% of all local cases reported during the two-week UCTP period. Compared with the CDPHS, the UCTP detected a higher proportion of sporadic cases (62% vs 27%, P<.01) and identified 6 (out of 18) additional clusters during that period. We estimated that 27% (95% credible interval: 22%, 34%) of all infections were detected by the CDPHS in the third wave. CONCLUSIONS: We reported empirical evidence of the utility of population-wide COVID-19 testing in detecting unrecognized infections and clusters. Around three quarters of infections have not been identified through existing surveillance approaches including contact tracing.

Genomic Epidemiology of Early SARS-CoV-2 Transmission Dynamics, Gujarat, India.

Limited genomic sampling in many high-incidence countries has impeded studies of severe respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic epidemiology. Consequently, critical questions remain about the generation and global distribution of virus genetic diversity. We investigated SARS-CoV-2 transmission dynamics in Gujarat, India, during the state's first epidemic wave to shed light on spread of the virus in one of the regions hardest hit by the pandemic. By integrating case data and 434 whole-genome sequences sampled across 20 districts, we reconstructed the epidemic dynamics and spatial spread of SARS-CoV-2 in Gujarat. Our findings indicate global and regional connectivity and population density were major drivers of the Gujarat outbreak. We detected >100 virus lineage introductions, most of which appear to be associated with international travel. Within Gujarat, virus dissemination occurred predominantly from densely populated regions to geographically proximate locations that had low population density, suggesting that urban centers contributed disproportionately to virus spread.