SARS-CoV-2 Omicron variants BA.4 and BA.5 dominated the fifth COVID-19 epidemiological wave in Mexico (preprint)

In Mexico, the BA.4 and BA.5 Omicron variants dominated the fifth epidemic wave (summer 2022), superseding BA.2, which had circulated during the inter-wave period. The present study uses genome sequencing and statistical and phylogenetic analyses to examine these variants’ prevalence, distribution, and genetic diversity in Mexico from April to August 2022. Over 35% of the sequenced genomes in this period corresponded to the BA.2 variant, 8% to the BA.4, and 56% to the BA.5 variant. Multiple subvariants were identified, but only BA.2.9, BA.2.12.1, BA.5.1, BA.5.2, BA.5.2.1, and BA.4.1 circulated throughout the fifth wave across the entire country, not forming geographical clusters. Contrastingly, other subvariants exhibited a geographically restricted distribution, most notably in the Southeast region, which showed a distinct subvariant dynamic. This study supports previous results showing that this region may be a major entry point and may have contributed to the introduction and evolution of novel variants in Mexico. Furthermore, a differential distribution was observed for certain subvariants among specific States throughout time, which may have contributed to the overall increased diversity observed during this wave compared to the previous one. This study highlights the importance of sustaining genomic surveillance to identify novel variants that may impact public health.

SARS-CoV-2 BW.1, a fast-growing Omicron variant from southeast Mexico bearing relevant escape mutations (preprint)

Purpose The swift growth of the BW.1 SARS-CoV-2 variant coincides with a new rapid increase of COVID-19 cases occurring in Southeast Mexico in October, 2022, putting an end to a period of low transmission after Mexico’s fifth epidemiological wave. Up to 75% of weekly sequenced genomes in the region have been identified as BW.1. In the current study, a comprehensive genomic comparison was carried out to characterize BW.1’s evolutionary history, identifying its origins and its most important mutations.Methods An alignment of all the genomes of BW.1 and its parental BA.5.6.2 variant was carried out to identify their mutations. A phylogenetic reconstruction and a longitudinal analysis of point mutations were performed to trace back their origin and contrast them with key RBD mutations in variant BQ.1, one of the fastest growing lineages to date.Results The BW.1’s genome derives from Mexican sequences of BA.5.6.2. Two traceable synonymous substitutions support its Mexican origin whereas other two are specific to BW.1: S:N460K and ORF1a:V627I. Mutations found in the receptor binding domain, S:K444T, S:L452R, S:N460K and S:F486V, in BW.1 have been reported to be relevant for immune escape and are key mutations in the BQ.1 lineage.Conclusions BW.1 appears to have arisen in the Yucatan Peninsula in Mexico sometime around July 2022 during the fifth COVID-19 wave. Its explosive growth may be in part explained by relevant escape mutations also found in BQ.1.

Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model (preprint)

Background SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS-CoV-2 infection incidences in several countries. At the start of December 2021 the first omicron cases were reported in France. Methods In this paper we investigate the contagiousness of this novel variant relatively to the delta variant that was also in circulation in France at that time. Using a dynamic multi-variant model accounting for cross-immunity through a status-based approach, we analyze screening data reported by Santé Publique France over 13 metropolitan French regions between 1st of December 2021 and the 30th of January 2022. During the investigated period, the delta variant was replaced by omicron in all metropolitan regions in approximately three weeks. The analysis conducted retrospectively allows us to consider the whole replacement time window and compare regions with different times of omicron introduction and baseline levels of variants’ transmission potential. As large uncertainties regarding cross-immunity among variants persist, uncertainty analyses were carried out to assess its impact on our estimations. Results Assuming that 80% of the population was immunized against delta, a cross delta/omicron cross-immunity of 25% and omicron generation time was 3.5 days, the relative strength of omicron to delta, expressed as the ratio of their respective reproduction rates, , was found to range between 1.51 and 1.86 across regions. Uncertainty analysis on epidemiological parameters led ranging over 1.57-2.13 when averaged over the metropolitan French regions, weighting by population size. Conclusions Upon introduction, omicron spread rapidly through the French territory and showed a high fitness relative to delta. We documented considerable geographical heterogeneities on the spreading dynamics. The historical reconstruction of variant emergence dynamics provide valuable ground knowledge to face future variant emergence events.

Targeting SARS-CoV-2 superspreading infections could dramatically improve the efficiency of epidemic control strategies in resource-limited contexts (preprint)

Background: Superspreading infections play an important role in the SARS-CoV-2 pandemic. Superspreading is caused primarily by heterogeneity in social contact rates, and therefore represents an opportunity for targeting surveillance and control via consideration of social network topologies, particularly in resource-limited settings. Yet, it remains unclear how to implement such surveillance and control, especially when network data is unavailable. Methods: We evaluated the efficiency of a testing strategy that targeted potential superspreading individuals based on their degree centrality on a social network compared to a random testing strategy in the context of low testing capacity. We simulated SARS-CoV-2 dynamics on two contact networks from rural Madagascar and measured the epidemic duration, infection burden, and tests needed to end the epidemics. In addition, we examined the robustness of this approach when individuals' true degree centralities were unknown and were instead estimated via readily-available socio-demographic variables. Findings: Targeted testing of potential superspreaders reduced the infection burden by 40-63% at low testing capacities, while requiring between 45-78% fewer tests compared to random testing. Further, targeted testing remained more efficient when the true network topology was unknown and prioritization was based on socio demographic characteristics. Interpretation: Incorporating social network topology into epidemic control strategies is an effective public health strategy for health systems suffering from low testing capacity and can be implemented via socio-demographic proxies when social networks are unknown.

Prioritizing COVID-19 vaccination efforts and dose allocation within Madagascar (preprint)

BackgroundWhile mass COVID-19 vaccination programs are underway in high-income countries, limited availability of doses has resulted in few vaccines administered in low and middle income countries (LMICs). The COVID-19 Vaccines Global Access (COVAX) is a WHO-led initiative to promote vaccine access equity to LMICs and is providing many of the doses available in these settings. However, initial doses are limited and countries, such as Madagascar, need to develop prioritization schemes to maximize the benefits of vaccination with very limited supplies. There is some consensus that dose deployment should initially target health care workers, and those who are more vulnerable including older individuals. However, questions of geographic deployment remain, in particular associated with limits around vaccine access and delivery capacity in underserved communities, for example in rural areas that may also include substantial proportions of the population. MethodsTo address these questions, we developed a mathematical model of SARS-CoV-2 transmission dynamics and simulated various vaccination allocation strategies for Madagascar. Simulated strategies were based on a number of possible geographical prioritization schemes, testing sensitivity to initial susceptibility in the population, and evaluating the potential of tests for previous infection. ResultsUsing cumulative deaths due to COVID-19 as the main outcome of interest, our results indicate that distributing the number of vaccine doses according to the number of elderly living in the region or according to the population size results in a greater reduction of mortality compared to distributing doses based on the reported number of cases and deaths. The benefits of vaccination strategies are diminished if the burden (and thus accumulated immunity) has been greatest in the most populous regions, but the overall strategy ranking remains comparable. If rapid tests for prior immunity may be swiftly and effectively delivered, there is potential for considerable gain in mortality averted, but considering delivery limitations modulates this. ConclusionAt a subnational scale, our results support the strategy adopted by the COVAX initiative at a global scale.

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic (preprint)

The effective reproduction number R eff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate R eff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its R eff (t) . Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We thus can estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active. For the third wave in Ireland the reduction was again significant (>70%). Author Summary In the early stages of any new epidemic, one of the first steps to design a control strategy is to estimate pathogen transmissibility in order to provide information on its potential to spread in the population. Among the different epidemiological indicators that characterize the transmissibility of a pathogen, the effective reproduction number R eff is commonly used for measuring time-varying transmissibility. It measures how many additional people can be infected by an infected individual during the course of an epidemic. However, R eff is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This is exactly the situation we are confronted with during this COVID-19 pandemic. The statistical methods classically used for the estimation of R eff have some shortcomings in the rigorous consideration of the transmission characteristics of SARS-CoV-2. We propose here to use an original approach based on a stochastic model whose parameters vary in time and are inferred in a Bayesian framework from reliable hospital data. This enables us to reconstruct both the COVID-19 epidemic and its R eff . The R eff time evolution allows us to get information regarding the potential effects of mitigation measures taken during and between epidemics waves. This approach, based on a stochastic model that realistically describes the hospital multiple datasets and which overcomes many of the biases associated with R eff estimates, appears to have some advantage over previously developed methods.

Identical trends of SARS-Cov-2 transmission and retail and transit mobility during non-lockdown periods (preprint)

Recent literature strongly supports the idea that mobility reduction and social distancing play a crucial role in transmission of SARS-Cov-2 infections. It was shown during the first wave that mobility restrictions reduce significantly infection transmission. Here we document the reverse relationship by showing, between the first two Covid-19 waves, a high positive correlation between the trends of SARS-Cov-2 transmission and mobility. These two trends oscillate simultaneously and increased mobility following lockdown relaxation has a significant positive relationship with increased transmission. From a public health perspective, these results highlight the importance of following the evolution of mobility when relaxing mitigation measures to anticipate the future evolution of the spread of the SARS-Cov-2.

Quantifying the efficiency of non-pharmaceutical interventions against SARS-COV-2 transmission in Europe (preprint)

Since the emergence of SARS-CoV-2, governments around the World have implemented a combination of public health responses based on non-pharmaceutical interventions (NPIs), with significant social and economic consequences. Though most European countries have overcome the first epidemic wave, it remains of high priority to quantify the efficiency of different NPIs to inform preparedness for an impending second wave. In this study, combining capture-recapture methods with Bayesian inference in an age-structured mathematical model, we use a unique European dataset compiled by the European Centre for Disease Control (ECDC) to quantify the efficiency of 24 NPIs and their combinations (referred to as public health responses, PHR) in reducing SARS-Cov-2 transmission rates in 32 European countries. Of 166 unique PHR tested, we found that median decrease in viral transmission was 74%, which is enough to suppress the epidemic. PHR efficiency was positively associated with the number of NPIs implemented. We found that bans on mass gatherings had the largest effect among NPIs, followed by school closures, teleworking, and stay home orders. Partial implementation of most NPIs resulted in lower than average response efficiency. This first large-scale estimation of NPI and PHR efficiency against SARS-COV-2 transmission in Europe suggests that a combination of NPIs targeting different population groups should be favored to control future epidemic waves.

Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: Insights from Madagascar (preprint)

The COVID-19 pandemic has wreaked havoc globally, and there has been a particular concern for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers high burdens of other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with very different implications for the final epidemic burden: (1) low case detection, (2) differences in COVID-19 epidemiology (e.g. low R0), and (3) policy interventions. The low number of cases to date have led some SSA governments to relax these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the current incidence of COVID-19 cases can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of an impending health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar until early 2021, hence the importance of conducting clinical trials and continually improving access to healthcare. ResumeLa pandemie de COVID-19 a eu des consequences nefastes partout dans le monde, et il y a une preoccupation particuliere pour lAfrique subsaharienne (ASS), ou des modeles suggerent que la majorite de la population sera infectee. Il est craint que le continent supportera un fardeau plus elevee de COVID-19 pour les memes raisons quil souffre davantage dautres maladies infectieuses: ecologie, conditions socio-economiques, manque dinfrastructures deau et dassainissement, et faiblesse des systemes de sante. Cependant, jusqua present, lASS a rapporte une incidence et une mortalite bien inferieure a celle des previsions des modeles pour cette region, ainsi quau nombre observe dans dautres regions du monde. Il y a trois explications principales pour ce phenomene, chacune ayant des implications tres differentes pour le fardeau epidemique final: (1) detection faible des cas, (2) differences dans lepidemiologie COVID-19 (par exemple faible R0), et (3) interventions et politiques mises en place. Le faible nombre de cas a ce jour a conduit certains gouvernements dASS a assouplir ces interventions. Cela entrainera-t-il une resurgence de cas? Pour comprendre comment interpreter le fait que les cas COVID-19 rapportes sont plus faibles que prevu a Madagascar, nous utilisons un modele de transmission structure par groupe dage pour explorer chacune de ces explications et predire limpact epidemique qui leur est associe. Nous montrons que lincidence actuelle des cas de COVID-19 peut sexpliquer par leffet cumule de lintroduction tardive des premiers cas importes, la mise en uvre rapide dinterventions non pharmaceutiques (INP) et de faibles taux de detection des cas. Cette analyse renforce le fait que Madagascar, ainsi que dautres pays dAfrique subsaharienne, reste a risque dune crise sanitaire imminente. Si les INP restent appliques, jusqua 50 000 vies pourraient etre sauvees. Meme avec des INP, tant quil ny aura pas des vaccins ni des nouvelles therapies efficaces, COVID-19 pourrait infecter jusqua 30% de la population, ce qui constituerait la plus grande menace pour la sante publique a Madagascar jusquau debut de 2021, dou limportance de la realisation des essais cliniques et de lamelioration continuelle de lacces aux soins. Summary BoxO_LIThe lower-than-expected number of reported cases of COVID-19 in Madagascar can be explained by a combination of the relatively late introduction of the disease, low detection rates, and low transmission rates due to the early and effective implementation of non-pharmaceutical interventions that reduced contact rates. C_LIO_LICOVID-19 is predicted to be the largest public health problem in Madagascar in 2020, but non-pharmaceutical interventions at an effectiveness similar to those seen in the first few months could save up to 50,000 lives. C_LIO_LIHealth systems in SSA remain at risk of an impending health crisis due to COVID-19, stressing the importance of ongoing clinical trials and improving health care access. C_LI

Optimal strategies for quarantine stopping in France. General expected patterns of strategies focusing on contact between age groups (preprint)

Due to the COVID-19 pandemic, many countries have implemented a complete lock-down of their population that may not be sustainable for long. To identify the best strategy to replace this full lock-down, sophisticated models that rely on mobility data have been developed. In this study, using the example of France as a case-study, we develop a simple model considering contacts between age classes to derive the general impact of partial lock-down strategies targeted at specific age groups. We found that epidemic suppression can only be achieved by targeting isolation of young and middle age groups with high efficiency. All other strategies tested result in a flatter epidemic curve, with outcomes in (e.g. mortality and health system over-capacity) dependent of the age groups targeted and the isolation efficiency. Targeting only the elderly can decrease the expected mortality burden, but in proportions lower than more integrative strategies involving several age groups. While not aiming to provide quantitative forecasts, our study shows the benefits and constraints of different partial lock-down strategies, which could help guide decision-making.