109 results found
Ang H, Menegale F, Preziosi G, et al., 2023, Reconstructing the impact of COVID-19 on the immunity gap and transmission of respiratory syncytial virus in Lombardy, Italy, EBioMedicine, Vol: 95, Pages: 1-11, ISSN: 2352-3964
BackgroundRespiratory syncytial virus (RSV) is a leading cause of hospitalisation and mortality in young children globally. The social distancing measures implemented against COVID-19 in Lombardy (Italy) disrupted the typically seasonal RSV circulation during 2019–2021 and caused substantially more hospitalisations during 2021–2022. The primary aim of this study is to quantify the immunity gap — defined as the increased proportion of the population naïve to RSV infection — following the relaxation of COVID-19 restrictions in Lombardy, which has been hypothesised to be a potential cause of the increased RSV burden in 2021–2022. Methods We developed a catalytic model to reconstruct changes in the age-dependent susceptibility profile of the Lombardy population throughout the COVID-19 pandemic. The model is calibrated to routinely collected hospitalisation, syndromic, and virological surveillance data and tested for alternative assumptions on age-dependencies in the risk of RSV infection throughout the pandemic. FindingsWe estimate that the proportion of the Lombardy population naïve to RSV infection increased by 60·8% (95% CrI: 55·2–65·4%) during the COVID-19 pandemic: from 1·4% (95% CrI: 1·3–1·6%) in 2018–2019 to 2·3% (95% CrI: 2·2–2·5%) before the 2021–2022 season, corresponding to an immunity gap of 0·87% (95% CrI: 0·87–0.88%). We found evidence of heterogeneity in RSV transmission by age, suggesting that the COVID-19 restrictions had variable impact on the contact patterns and risk of RSV infection across ages. Interpretation We estimate a substantial increase in the population-level susceptibility to RSV in Lombardy during 2019–2021, which contributed to an increase in primary RSV infections in 2021–2022.
McCormack CP, Yan AWC, Brown JC, et al., 2023, Modelling the viral dynamics of the SARS-CoV-2 Delta and Omicron variants in different cell types., Journal of the Royal Society Interface, Vol: 20, Pages: 1-12, ISSN: 1742-5662
We use viral kinetic models fitted to viral load data from in vitro studies to explain why the SARS-CoV-2 Omicron variant replicates faster than the Delta variant in nasal cells, but slower than Delta in lung cells, which could explain Omicron's higher transmission potential and lower severity. We find that in both nasal and lung cells, viral infectivity is higher for Omicron but the virus production rate is higher for Delta, with an estimated approximately 200-fold increase in infectivity and 100-fold decrease in virus production when comparing Omicron with Delta in nasal cells. However, the differences are unequal between cell types, and ultimately lead to the basic reproduction number and growth rate being higher for Omicron in nasal cells, and higher for Delta in lung cells. In nasal cells, Omicron alone can enter via a TMPRSS2-independent pathway, but it is primarily increased efficiency of TMPRSS2-dependent entry which accounts for Omicron's increased activity. This work paves the way for using within-host mathematical models to understand the transmission potential and severity of future variants.
Caicedo E-Y, Charniga K, Rueda A, et al., 2023, Correction: The epidemiology of Mayaro virus in the Americas: A systematic review and key parameter estimates for outbreak modelling., PLoS Neglected Tropical Diseases, Vol: 17, Pages: 1-2, ISSN: 1935-2727
[This corrects the article DOI: 10.1371/journal.pntd.0009418.].
Lin C-P, Dorigatti I, Tsui K-L, et al., 2022, Impact of early phase COVID-19 precautionary behaviours on seasonal influenza in Hong Kong: a time-series modelling approach, Frontiers in Public Health, Vol: 10, Pages: 1-10, ISSN: 2296-2565
Background: Before major non-pharmaceutical interventions were implemented, seasonal influenza incidence in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumablyassociated with precautionary behavioural changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza could inform future influenza prevention strategies.Methods: We estimated the effective reproduction number (Rt) of seasonal influenza in 2019/20 winter using a Time-Series Susceptible-Infectious-Recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in under-reporting and herd immunity, the individual effects of the behavioural changes were calculated.Findings: The model-estimated mean Rt reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% (95%CI, 16.3%-18.3%) from infections, about half of the effect of avoiding crowded places (44.1%,95%CI, 43.5%-44.7%). Within the current model, if more than 85% of people had adopted both behaviours, the initial Rt could have been less than 1.Conclusion: Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.
Del Vecchio C, Cracknell Daniels B, Brancaccio G, et al., 2022, Impact of antigen test target failure and testing strategies on the transmission of SARS-CoV-2 variants, Nature Communications, Vol: 13, Pages: 1-16, ISSN: 2041-1723
Population testing remains central to COVID-19 control and surveillance, with countries increasingly using antigen tests rather than molecular tests. Here we describe a SARS-CoV-2 variant that escapes N antigen tests due to multiple disruptive amino-acid substitutions in the N protein. By fitting a multistrain compartmental model to genomic and epidemiological data, we show that widespread antigen testing in the Italian region of Veneto favored the undetected spread of the antigen-escape variant compared to the rest of Italy. We highlight novel limitations of widespread antigen testing in the absence of molecular testing for diagnostic or confirmatory purposes. Notably, we find that genomic surveillance systems which rely on antigen population testing to identify samples for sequencing will bias detection of escape antigen test variants. Together, these findings highlight the importance of retaining molecular testing for surveillance purposes, including in contexts where the use of antigen tests is widespread.
Cox V, O'Driscoll M, Imai N, et al., 2022, Estimating dengue transmission intensity from serological data: a comparative analysis using mixture and catalytic models., PLoS Neglected Tropical Diseases, Vol: 16, Pages: e0010592-e0010592, ISSN: 1935-2727
BACKGROUND: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI. METHODS: We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194). RESULTS: The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models. CONCLUSIONS: Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.
Brizzi A, O'Driscoll M, Dorigatti I, 2022, Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics, Clinical Infectious Diseases, Vol: 75, Pages: e114-e121, ISSN: 1058-4838
Background:Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. The reasons driving the observed bias are unknown. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase.Methods:We propose a debiasing procedure which utilises a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to SARS-CoV-2 incidence data reported in Italy, Sweden, the United Kingdom and the United States of America.Results:In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real world data, the proposed adjustment produces basic reproduction number estimates which closely match the estimates obtained in other studies while making use of a minimal amount of data.Conclusions:The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.
Lavezzo E, Pacenti M, Manuto L, et al., 2022, Neutralising reactivity against SARS-CoV-2 delta and omicron variants by vaccination and infection history., Genome Medicine: medicine in the post-genomic era, Vol: 14, Pages: 61-61, ISSN: 1756-994X
BACKGROUND: The continuous emergence of SARS-CoV-2 variants of concern (VOC) with immune escape properties, such as Delta (B.1.617.2) and Omicron (B.1.1.529), questions the extent of the antibody-mediated protection against the virus. Here we investigated the long-term antibody persistence in previously infected subjects and the extent of the antibody-mediated protection against B.1, B.1.617.2 and BA.1 variants in unvaccinated subjects previously infected, vaccinated naïve and vaccinated previously infected subjects. METHODS: Blood samples collected 15 months post-infection from unvaccinated (n=35) and vaccinated (n=41) previously infected subjects (Vo' cohort) were tested for the presence of antibodies against the SARS-CoV-2 spike (S) and nucleocapsid (N) antigens using the Abbott, DiaSorin, and Roche immunoassays. The serum neutralising reactivity was assessed against B.1, B.1.617.2 (Delta), and BA.1 (Omicron) SARS-CoV-2 strains through micro-neutralisation. The antibody titres were compared to those from previous timepoints, performed at 2- and 9-months post-infection on the same individuals. Two groups of naïve subjects were used as controls, one from the same cohort (unvaccinated n=29 and vaccinated n=20) and a group of vaccinated naïve healthcare workers (n=61). RESULTS: We report on the results of the third serosurvey run in the Vo' cohort. With respect to the 9-month time point, antibodies against the S antigen significantly decreased (P=0.0063) among unvaccinated subjects and increased (P<0.0001) in vaccinated individuals, whereas those against the N antigen decreased in the whole cohort. When compared with control groups (naïve Vo' inhabitants and naïve healthcare workers), vaccinated subjects that were previously infected had higher antibody levels (P<0.0001) than vaccinated naïve subjects. Two doses of vaccine elicited stronger anti-S antibody response than natural infection (P<0.0001). Finally, the neutralising rea
Okell L, Brazeau NF, Verity R, et al., 2022, Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling, Communications Medicine, Vol: 2, Pages: 1-13, ISSN: 2730-664X
Background: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion. Methods: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries. Results: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49 -2.53%.Conclusion: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.
Manuto L, Grazioli M, Spitaleri A, et al., 2022, Rapid SARS-CoV-2 intra-host and within-household emergence of novel haplotypes, Viruses, Vol: 14, Pages: 1-14, ISSN: 1999-4915
In February 2020, the municipality of Vo’, a small town near Padua (Italy) was quarantined due to the first coronavirus disease 19 (COVID-19)-related death detected in Italy. To investigate the viral prevalence and clinical features, the entire population was swab tested in two sequential surveys. Here we report the analysis of 87 viral genomes, which revealed that the unique ancestor haplotype introduced in Vo’ belongs to lineage B, carrying the mutations G11083T and G26144T. The viral sequences allowed us to investigate the viral evolution while being transmitted within and across households and the effectiveness of the non-pharmaceutical interventions implemented in Vo’. We report, for the first time, evidence that novel viral haplotypes can naturally arise intra-host within an interval as short as two weeks, in approximately 30% of the infected individuals, regardless of symptom severity or immune system deficiencies. Moreover, both phylogenetic and minimum spanning network analyses converge on the hypothesis that the viral sequences evolved from a unique common ancestor haplotype that was carried by an index case. The lockdown extinguished both the viral spread and the emergence of new variants.
Sangkaew S, Ming D, Boonyasiri A, et al., 2021, Transaminases and serum albumin as early predictors of severe dengue reply, Lancet Infectious Diseases, Vol: 21, Pages: 1489-1490, ISSN: 1473-3099
Forna A, Dorigatti I, Nouvellet P, et al., 2021, Comparison of machine learning methods for estimating case fatality ratios: an Ebola outbreak simulation study, PLoS One, Vol: 16, ISSN: 1932-6203
BackgroundMachine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous.MethodsUsing simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random—MCAR, missing at random—MAR, or missing not at random—MNAR).ResultsAcross ML methods, dataset sizes and proportions of training data used, the area under the receiver operating characteristic curve decreased by 7% (median, range: 1%–16%) when missingness was increased from 10% to 40%. Overall reduction in CFR bias for MAR across methods, proportion of missingness, outbreak size and proportion of training data was 0.5% (median, range: 0%–11%).ConclusionML methods could reduce bias and increase the precision in CFR estimates at low levels of missingness. However, no method is robust to high percentages of missingness. Thus, a datacentric approach is recommended in outbreak settings—patient survival outcome data should be prioritised for collection and random-sample follow-ups should be implemented to ascertain missing outcomes.
Dorigatti I, Lavezzo E, Manuto L, et al., 2021, SARS-CoV-2 antibody dynamics and transmission from community-wide serological testing in the Italian municipality of Vo' (vol 12, 4383, 2021), Nature Communications, Vol: 12, Pages: 1-1, ISSN: 2041-1723
Dorigatti I, Lavezzo E, Manuto L, et al., 2021, SARS-CoV-2 antibody dynamics and transmission from community-wide serological testing in the Italian municipality of Vo’, Nature Communications, Vol: 12, Pages: 1-11, ISSN: 2041-1723
In February and March 2020, two mass swab testing campaigns were conducted in Vo’, Italy. In May 2020, we tested 86% of the Vo’ population with three immuno-assays detecting antibodies against the spike and nucleocapsid antigens, a neutralisation assay and Polymerase Chain Reaction (PCR). Subjects testing positive to PCR in February/March or a serological assay in May were tested again in November. Here we report on the results of the analysis of the May and November surveys. We estimate a seroprevalence of 3.5% (95% Credible Interval (CrI): 2.8%-4.3%) in May. In November, 98.8% (95% Confidence Interval (CI): 93.7%-100.0%) of sera which tested positive in May still reacted against at least one antigen; 18.6% (95%CI:11.0%-28.5%) showed an increase of antibody or neutralisation reactivity from May. Analysis of the serostatus of the members of 1,118 households indicates a 26.0% (95%CrI:17.2%-36.9%) Susceptible-Infectious Transmission Probability. Contact tracing had limited impact on epidemic suppression.
Laydon D, Dorigatti I, Small R, et al., 2021, Efficacy profile of the CYD-TDV dengue vaccine revealed by Bayesian survival analysis of individual-level Phase III data, eLife, Vol: 10, ISSN: 2050-084X
Background: Sanofi-Pasteur’s CYD-TDV is the only licensed dengue vaccine. Two phase III trials showed higher efficacy in seropositive than seronegative recipients. Hospital follow-up revealed increased hospitalisation in 2-5-year-old vaccinees, where serostatus and age effects were unresolved.Methods: We fit a survival model to individual-level data from both trials, including year one of hospital follow-up. We determine efficacy by age, serostatus, serotype and severity, and examine efficacy duration and vaccine action mechanism.Results: Our modelling indicates that vaccine-induced immunity is long-lived in seropositive recipients, and therefore that vaccinating seropositives gives higher protection than two natural infections. Long-term increased hospitalisation risk outweighs short-lived immunity in seronegatives. Independently of serostatus, transient immunity increases with age, and is highest against serotype 4. Benefit is higher in seropositives, and risk enhancement is greater in seronegatives, against hospitalised disease than febrile disease.Conclusions: Our results support vaccinating seropositives only. Rapid diagnostic tests would enable viable “screen-then-vaccinate” programs. Since CYD-TDV acts as a silent infection, long-term safety of other vaccine candidates must be closely monitored.Funding: Bill and Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust.
O'Driscoll M, Harry C, Donnelly CA, et al., 2021, A comparative analysis of statistical methods to estimate the reproduction number in emerging epidemics with implications for the current COVID-19 pandemic, Clinical Infectious Diseases, Vol: 73, Pages: e215-e223, ISSN: 1058-4838
As the SARS-CoV-2 pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the reproduction number, R0, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts. Using simulated epidemic data we assess the performance of 6 commonly-used statistical methods to estimate R0 as they would be applied in a real-time outbreak analysis scenario - fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015-2016 epidemic in Latin America and the Caribbean. We find that all methods considered here frequently over-estimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts. We show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision, particularly for data with significant over-dispersion. As localised epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts.
Sangkaew S, Ming D, Boonyasiri A, et al., 2021, Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis, Lancet Infectious Diseases, Vol: 21, Pages: 1014-1026, ISSN: 1473-3099
BACKGROUND: The ability to accurately predict early progression of dengue to severe disease is crucial for patient triage and clinical management. Previous systematic reviews and meta-analyses have found significant heterogeneity in predictors of severe disease due to large variation in these factors during the time course of the illness. We aimed to identify factors associated with progression to severe dengue disease that are detectable specifically in the febrile phase. METHODS: We did a systematic review and meta-analysis to identify predictors identifiable during the febrile phase associated with progression to severe disease defined according to WHO criteria. Eight medical databases were searched for studies published from Jan 1, 1997, to Jan 31, 2020. Original clinical studies in English assessing the association of factors detected during the febrile phase with progression to severe dengue were selected and assessed by three reviewers, with discrepancies resolved by consensus. Meta-analyses were done using random-effects models to estimate pooled effect sizes. Only predictors reported in at least four studies were included in the meta-analyses. Heterogeneity was assessed using the Cochrane Q and I2 statistics, and publication bias was assessed by Egger's test. We did subgroup analyses of studies with children and adults. The study is registered with PROSPERO, CRD42018093363. FINDINGS: Of 6643 studies identified, 150 articles were included in the systematic review, and 122 articles comprising 25 potential predictors were included in the meta-analyses. Female patients had a higher risk of severe dengue than male patients in the main analysis (2674 [16·2%] of 16 481 vs 3052 [10·5%] of 29 142; odds ratio [OR] 1·13 [95% CI 1·01-1·26) but not in the subgroup analysis of studies with children. Pre-existing comorbidities associated with severe disease were diabetes (135 [31·3%] of 431 with vs 868 [16·0%] of 5421 witho
Smith TP, Flaxman S, Gallinat AS, et al., 2021, Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions, Proceedings of the National Academy of Sciences of USA, Vol: 118, Pages: 1-8, ISSN: 0027-8424
As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention (“lockdown”) and reductions in individuals’ mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.
Caicedo Y, Charniga K, Rueda A, et al., 2021, The epidemiology of Mayaro virus in the Americas: a systematic review and key parameter estimates for outbreak modelling, PLoS Neglected Tropical Diseases, Vol: 15, ISSN: 1935-2727
Mayaro virus (MAYV) is an arbovirus that is endemic to tropical forests in Central and South America, particularly within the Amazon basin. In recent years, concern has increased regarding MAYV’s ability to invade urban areas and cause epidemics across the region. We conducted a systematic literature review to characterise the evolutionary history of MAYV, its transmission potential, and exposure patterns to the virus. We analysed data from the literature on MAYV infection to produce estimates of key epidemiological parameters, including the generation time and the basic reproduction number, R0. We also estimated the force-of-infection (FOI) in epidemic and endemic settings. Seventy-six publications met our inclusion criteria. Evidence of MAYV infection in humans, animals, or vectors was reported in 14 Latin American countries. Nine countries reported evidence of acute infection in humans confirmed by viral isolation or reverse transcription-PCR (RT-PCR). We identified at least five MAYV outbreaks. Seroprevalence from population based cross-sectional studies ranged from 21% to 72%. The estimated mean generation time of MAYV was 15.2 days (95% CrI: 11.7–19.8) with a standard deviation of 6.3 days (95% CrI: 4.2–9.5). The per-capita risk of MAYV infection (FOI) ranged between 0.01 and 0.05 per year. The mean R0 estimates ranged between 2.1 and 2.9 in the Amazon basin areas and between 1.1 and 1.3 in the regions outside of the Amazon basin. Although MAYV has been identified in urban vectors, there is not yet evidence of sustained urban transmission. MAYV’s enzootic cycle could become established in forested areas within cities similar to yellow fever virus.
Dorigatti I, Lavezzo E, Manuto L, et al., 2021, SARS-CoV-2 antibody dynamics, within-household transmission and the impact of contact tracing from community-wide serological testing in the Italian Municipality of Vo’, Publisher: Elsevier BV
Background: In February and Mach 2020, two mass swab testing campaigns conducted in Vo’, Italy demonstrated the extent of asymptomatic SARS-CoV-2 infection and the feasibility of epidemic suppression.Methods: We tested 86% of the Vo’ population (2,602 subjects) in May with three immuno-assays detecting antibodies against the spike (S) and nucleocapsid (N) antigens, a neutralisation assay and Polymerase Chain Reaction (PCR). Subjects testing positive to PCR in February/March or a serological assay in May were tested again in November.Findings: Combining the results obtained with the three assays, we estimate a seroprevalence of 3.5% (95% Credible Interval (CrI) 2.8%-4.3%) in May. In November, all assays showed a reduction in antibody titres, though 98.8% (95% Confidence Interval (CI) 93.7%-100.0%) of sera still reacted against at least one antigen. Conversely, 18.6% (95% CI 11.0%-28.5%) showed a marked increase of antibody or viral neutralisation reactivity between May and November, linked to documented or likely re-exposures. We found significant differences in the magnitude and persistence of the antibody response by age group but not by symptom occurrence, hospitalisation, or sex. Analysis of the serostatus of 1,118 households indicated a 27.3% (95% CrI 19.2%-34.6%) probability of SARS-CoV-2 transmission among household members and that 81.8% (95% CrI 55.9%-95.2%) of transmission could be attributed to 20% of infections. Contact tracing correctly identified 44% of the infected subjects and had limited impact on the epidemic.Interpretation: We find evidence of antibody persistence up to nine months post infection. Different assays provided significantly different seroprevalence estimates, making it challenging to compare seroprevalence estimates globally. Due to the high population susceptibility and the limited impact of contact tracing, rigorous testing and improvements in contact tracing are essential to control SARS-CoV-2.Funding: Veneto Region, Med
Lau K, Dorigatti I, Miraldo M, et al., 2021, SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals, International Journal of Infectious Diseases, Vol: 105, Pages: 161-171, ISSN: 1201-9712
ObjectiveThe COVID-19 pandemic demonstrates the need for understanding pathways to healthcare demand, morbidity, and mortality of pandemic patients. We estimate H1N1 (1) hospitalization rates, (2) severity rates (length of stay, ventilation, pneumonia, and death) of those hospitalized, (3) mortality rates, and (4) time lags between infections and hospitalizations during the pandemic (June 2009 to March 2010) and post-pandemic influenza season (November 2010 to February 2011) in England.MethodsEstimates of H1N1 infections from a dynamic transmission model are combined with hospitalizations and severity using time series econometric analyses of administrative patient-level hospital data.ResultsHospitalization rates were 34% higher and severity rates of those hospitalized were 20%–90% higher in the post-pandemic period than the pandemic. Adults (45–64-years-old) had the highest ventilation and pneumonia hospitalization rates. Hospitalizations did not lag infection during the pandemic for the young (<24-years-old) but lagged by one or more weeks for all ages in the post-pandemic period.DiscussionThe post-pandemic flu season exhibited heightened H1N1 severity, long after the pandemic was declared over. Policymakers should remain vigilant even after pandemics seem to have subsided. Analysis of administrative hospital data and epidemiological modelling estimates can provide valuable insights to inform responses to COVID-19 and future influenza and other disease pandemics.
BackgroundThere is concern about the risk of yellow fever (YF) establishment in Asia, owing to rising numbers of urban outbreaks in endemic countries and globalisation. Following an outbreak in Angola in 2016, YF cases were introduced into China. Prior to this, YF had never been recorded in Asia, despite climatic suitability and the presence of mosquitoes. An outbreak in Asia could result in widespread fatalities and huge economic impact. Therefore, quantifying the potential risk of YF outbreaks in Asia is a public health priority.MethodsUsing international flight data and YF incidence estimates from 2016, we quantified the risk of YF introduction via air travel into Asia. In locations with evidence of a competent mosquito population, the potential for autochthonous YF transmission was estimated using a temperature-dependent model of the reproduction number and a branching process model assuming a negative binomial distribution.ResultsIn total, 25 cities across Asia were estimated to be at risk of receiving at least one YF viraemic traveller during 2016. At their average temperatures, we estimated the probability of autochthonous transmission to be <50% in all cities, which was primarily due to the limited number of estimated introductions that year.ConclusionDespite the rise in air travel, we found low support for travel patterns between YF endemic countries and Asia resulting in autochthonous transmission during 2016. This supports the historic absence of YF in Asia and suggests it could be due to a limited number of introductions in previous years. Future increases in travel volumes or YF incidence can increase the number of introductions and the risk of autochthonous transmission. Given the high proportion of asymptomatic or mild infections and the challenges of YF surveillance, our model can be used to estimate the introduction and outbreak risk and can provide useful information to surveillance systems.
Smith TP, Dorigatti I, Mishra S, et al., 2021, Environmental drivers of SARS-CoV-2 lineage B.1.1.7 transmission intensity
<jats:title>Abstract</jats:title><jats:p>Previous work has shown that environment affects SARS-CoV-2 transmission, but it is unclear whether emerging strains show similar responses. Here we show that, like other SARS-CoV-2 strains, lineage B.1.1.7 spread with greater transmission in colder and more densely populated parts of England. However, we also find evidence of B.1.1.7 having a transmission advantage at warmer temperatures compared to other strains. This implies that spring and summer conditions are unlikely to slow B.1.1.7’s invasion in Europe and across the Northern hemisphere - an important consideration for public health interventions.</jats:p>
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts.Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world.Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation.In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
Verity R, Okell L, Dorigatti I, et al., 2021, COVID-19 and the difficulty of inferring epidemiological parameters from clinical data Reply, LANCET INFECTIOUS DISEASES, Vol: 21, Pages: 28-28, ISSN: 1473-3099
Fu H, Wang H, Xi X, et al., 2021, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, Vol: 102, Pages: 463-471, ISSN: 1201-9712
Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.
Caicedo E-Y, Charniga K, Rueda A, et al., 2020, The epidemiology of Mayaro virus in the Americas: A systematic review and key parameter estimates for outbreak modelling, Publisher: Public Library of Science
<jats:title>Abstract</jats:title><jats:p>Mayaro virus (MAYV) is an arbovirus that is endemic to tropical forests in Central and South America, particularly within the Amazon basin. In recent years, concern has increased regarding MAYV’s ability to invade urban areas and cause epidemics across the region. We conducted a systematic literature review to characterise the evolutionary history of MAYV, its transmission potential, and exposure patterns to the virus. We analysed data from the literature on MAYV infection to produce estimates of key epidemiological parameters, including the generation time and the basic reproduction number, <jats:italic>R</jats:italic><jats:sub>0</jats:sub>. We also estimated the force-of-infection (FOI) in epidemic and endemic settings. Seventy-six publications met our inclusion criteria. Evidence of MAYV infection in humans, animals, or vectors was reported in 14 Latin American countries. Nine countries reported evidence of acute infection in humans confirmed by viral isolation or reverse transcription-PCR (RT-PCR). We identified at least five MAYV outbreaks. Seroprevalence from population based cross-sectional studies ranged from 21% to 72%. The estimated mean generation time of MAYV was 15.2 days (95% CrI: 11.7-19.8) with a standard deviation of 6.3 days (95% CrI: 4.2-9.5). The per-capita risk of MAYV infection (FOI) ranged between 0.01 and 0.05 per year, producing <jats:italic>R</jats:italic><jats:sub>0</jats:sub> estimates between 1.1 and 2.9 in endemic settings. In an outbreak in Santa Cruz, Bolivia, <jats:italic>R</jats:italic><jats:sub>0</jats:sub> was estimated at 2.2 (95% CrI: 0.8-4.8). Although MAYV has been identified in urban vectors, there is not yet evidence of sustained urban transmission. MAYV’s enzootic cycle could become established in forested areas within cities similar to yellow fever virus.</jats:p><jats:sec&
Unwin H, Mishra S, Bradley V, et al., 2020, State-level tracking of COVID-19 in the United States, Nature Communications, Vol: 11, Pages: 1-9, ISSN: 2041-1723
As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available deathdata within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on therate of transmission of SARS-CoV-2. We estimate thatRtwas only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.
Thompson H, Imai N, Dighe A, et al., 2020, SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China, Journal of Travel Medicine, Vol: 27, Pages: 1-3, ISSN: 1195-1982
We estimated SARS-CoV-2 infection prevalence in cohorts of repatriated citizens from Wuhan to be 0.44% (95% CI: 0.19%–1.03%). Although not representative of the wider population we believe these estimates are helpful in providing a conservative estimate of infection prevalence in Wuhan City, China, in the absence of large-scale population testing early in the epidemic.
Sangkaew S, 2020, Enhancing risk prediction of progression to severe disease during the febrile phase of dengue: A systematic review and meta-analysis, The Lancet Infectious Diseases, Vol: 101, Pages: 237-238, ISSN: 1473-3099
Background: Since no effective vaccine or specific treatment for dengue exists, the early prediction of progression to severe disease plays a keys role in patient triage and clinical management during the febrile phase. Without differentiating the time-course of the illness, previous systematic reviews and meta-analyses may have failed to identify early prognostic factors for progression to severe disease. This study aimed to identify the factors associated with progression to severe dengue disease, which are detectable specifically in the febrile phase.Methods and materials: We conducted a systematic review and meta-analysis to identify prognostic factors associated with disease progression identifiable during the febrile phase. Eight medical databases including MEDLINE, EMBASE, and Web of Science were searched for studies published from January 1997 to February 2018. The relevant studies were selected and assessed by three reviewers independenly with discrepancies resolved by consensus. We performed meta-analysis for factors reported in at least four studies. Meta-analysis were performed using random-effects models; heterogeneity and publication bias were also assessed.Results: In line with the 2009 WHO guidelines, we found that vomiting, abdominal pain and tenderness, spontaneous and mucosal bleeding, and clinical fluid accumulation were clinical features associated with severe disease. In addition, we found that the presence of specific pre-existing comorbidities (diabetes mellitus, hypertension and renal disease) were associated with progression to severe disease. We also found that individuals with a lower platelet count, lower serum albumin and higher aminotransferase levels (AST or ALT), detected during the first four days of the illness, were more prone to progress to severe disease. Dengue virus serotype 2 and secondary infections were also associated with progression to severe disease.Conclusion: This study supports the monitoring of the warning signs des
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