A scoping literature review of global dengue age-stratified seroprevalence data: estimating dengue force of infection in endemic countries.

BACKGROUND: Dengue poses a significant burden worldwide, and a more comprehensive understanding of the heterogeneity in the intensity of dengue transmission within endemic countries is necessary to evaluate the potential impact of public health interventions. METHODS: This scoping literature review aimed to update a previous study of dengue transmission intensity by collating global age-stratified dengue seroprevalence data published in the Medline, Embase and Web of Science databases from 2014 to 2023. These data were then utilised to calibrate catalytic models and estimate the force of infection (FOI), which is the yearly per-capita risk of infection for a typical susceptible individual. FINDINGS: We found a total of 66 new publications containing 219 age-stratified seroprevalence datasets across 30 endemic countries. Together with the previously available average FOI estimates, there are now more than 250 dengue average FOI estimates obtained from seroprevalence studies from across the world. INTERPRETATION: The results show large heterogeneities in average dengue FOI both across and within countries. These new estimates can be used to inform ongoing modelling efforts to improve our understanding of the drivers of the heterogeneity in dengue transmission globally, which in turn can help inform the optimal implementation of public health interventions. FUNDING: UK Medical Research Council, Wellcome Trust, Community Jameel, Drugs for Neglected Disease initiative (DNDi) funded by the French Development Agency, Médecins Sans Frontières International; Swiss Agency for Development and Cooperation and UK aid.

Modelling the impact of JNJ-1802, a first-in-class dengue inhibitor blocking the NS3-NS4B interaction, on in-vitro DENV-2 dynamics.

Dengue virus (DENV) is a public health challenge across the tropics and subtropics. Currently, there is no licensed prophylactic or antiviral treatment for dengue. The novel DENV inhibitor JNJ-1802 can significantly reduce viral load in mice and non-human primates. Here, using a mechanistic viral kinetic model calibrated against viral RNA data from experimental in-vitro infection studies, we assess the in-vitro inhibitory effect of JNJ-1802 by characterising infection dynamics of two DENV-2 strains in the absence and presence of different JNJ-1802 concentrations. Viral RNA suppression to below the limit of detection was achieved at concentrations of >1.6 nM, with a median concentration exhibiting 50% of maximal inhibitory effect (IC50) of 1.23x10-02 nM and 1.28x10-02 nM for the DENV-2/RL and DENV-2/16681 strains, respectively. This work provides important insight into the in-vitro inhibitory effect of JNJ-1802 and presents a first step towards a modelling framework to support characterization of viral kinetics and drug effect across different host systems.

A simulation-based method to inform serosurvey design for estimating the force of infection using existing blood samples.

The extent to which dengue virus has been circulating globally and especially in Africa is largely unknown. Testing available blood samples from previous cross-sectional serological surveys offers a convenient strategy to investigate past dengue infections, as such serosurveys provide the ideal data to reconstruct the age-dependent immunity profile of the population and to estimate the average per-capita annual risk of infection: the force of infection (FOI), which is a fundamental measure of transmission intensity. In this study, we present a novel methodological approach to inform the size and age distribution of blood samples to test when samples are acquired from previous surveys. The method was used to inform SERODEN, a dengue seroprevalence survey which is currently being conducted in Ghana among other countries utilizing samples previously collected for a SARS-CoV-2 serosurvey. The method described in this paper can be employed to determine sample sizes and testing strategies for different diseases and transmission settings.

Epidemiological and genomic investigation of chikungunya virus in Rio de Janeiro state, Brazil, between 2015 and 2018.

Since 2014, Brazil has experienced an unprecedented epidemic caused by chikungunya virus (CHIKV), with several waves of East-Central-South-African (ECSA) lineage transmission reported across the country. In 2018, Rio de Janeiro state, the third most populous state in Brazil, reported 41% of all chikungunya cases in the country. Here we use evolutionary and epidemiological analysis to estimate the timescale of CHIKV-ECSA-American lineage and its epidemiological patterns in Rio de Janeiro. We show that the CHIKV-ECSA outbreak in Rio de Janeiro derived from two distinct clades introduced from the Northeast region in mid-2015 (clade RJ1, n = 63/67 genomes from Rio de Janeiro) and mid-2017 (clade RJ2, n = 4/67). We detected evidence for positive selection in non-structural proteins linked with viral replication in the RJ1 clade (clade-defining: nsP4-A481D) and the RJ2 clade (nsP1-D531G). Finally, we estimate the CHIKV-ECSA's basic reproduction number (R0) to be between 1.2 to 1.6 and show that its instantaneous reproduction number (Rt) displays a strong seasonal pattern with peaks in transmission coinciding with periods of high Aedes aegypti transmission potential. Our results highlight the need for continued genomic and epidemiological surveillance of CHIKV in Brazil, particularly during periods of high ecological suitability, and show that selective pressures underline the emergence and evolution of the large urban CHIKV-ECSA outbreak in Rio de Janeiro.

Reconstructing the impact of COVID-19 on the immunity gap and transmission of respiratory syncytial virus in Lombardy, Italy.

BACKGROUND: Respiratory 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. FINDINGS: We 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. FUNDING: UK Medical Research Council (MRC), UK Foreign, Commonwealth & Development Office (FCDO), EDCTP2 programme, European Union, Wellcome Trust, Royal Society, EU-MUR PNRR INF-ACT.

Modelling the viral dynamics of the SARS-CoV-2 Delta and Omicron variants in different cell types.

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.

Correction: The epidemiology of Mayaro virus in the Americas: A systematic review and key parameter estimates for outbreak modelling.

[This corrects the article DOI: 10.1371/journal.pntd.0009418.].

Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach.

Background: Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza 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 presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies. Methods: We estimated the effective reproduction number (R t ) 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 underreporting and herd immunity, the individual effects of the behavioral changes were quantified. Findings: The model-estimated mean R t 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% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R t 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.

Impact of antigen test target failure and testing strategies on the transmission of SARS-CoV-2 variants.

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.

Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.

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.

Neutralising reactivity against SARS-CoV-2 Delta and Omicron variants by vaccination and infection history.

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 reactivity of sera against B.1.617.2 and BA.1 was 4-fold and 16-fold lower than the reactivity observed against the original B.1 strain. CONCLUSIONS: These results confirm that vaccination induces strong antibody response in most individuals, and even stronger in previously infected subjects. Neutralising reactivity elicited by natural infection followed by vaccination is increasingly weakened by the recent emergence of VOCs. While immunity is not completely compromised, a change in vaccine development may be required going forward, to generate cross-protective pan-coronavirus immunity in the global population.

Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling.

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.

Refining Reproduction Number Estimates to Account for Unobserved Generations of Infection in Emerging Epidemics.

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. 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 that utilizes 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 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data reported in Italy, Sweden, the United Kingdom, and the United States. 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 that 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.

Rapid SARS-CoV-2 Intra-Host and Within-Household Emergence of Novel Haplotypes.

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.

Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.

BACKGROUND: Machine 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. METHODS: Using 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). RESULTS: Across 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%). CONCLUSION: ML 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.

Efficacy profile of the CYD-TDV dengue vaccine revealed by Bayesian survival analysis of individual-level phase III data.

Background: Sanofi-Pasteur's CYD-TDV is the only licensed dengue vaccine. Two phase three 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 1 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 against 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 & Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust, Royal Society. Clinical trial number: NCT01373281 and NCT01374516.

SARS-CoV-2 antibody dynamics and transmission from community-wide serological testing in the Italian municipality of Vo'.

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.

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions.

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.