Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life: an application to settings in Europe, Asia, Africa, and South America (preprint)

Long-term COVID-19 complications are a globally pervasive threat, but their plausible social drivers are often not prioritized. Here, we use data from a multinational consortium to quantify the relative contributions of social and clinical factors to differences in quality of life among participants experiencing long COVID and measure the extent to which social variables impacts can be attributed to clinical intermediates, across diverse contexts. In addition to age, neuropsychological and rheumatological comorbidities, educational attainment, employment status, and female sex were identified as important predictors of long COVID-associated quality of life days (long COVID QALDs). Furthermore, a great majority of their impacts on long COVID QALDs could not be tied to key long COVID-predicting comorbidities, such as asthma, diabetes, hypertension, psychological disorder, and obesity. In Norway, 90% (95% CI: 77%, 100%) of the effect of belonging to the highest versus lowest educational attainment quintile was not attributed to intermediate comorbidity impacts. The same was true for 86% (73%, 100%) of the protective effects of full-time employment versus all other employment status categories (excluding retirement) in the UK and 74% (46%,100%) of the protective effects of full-time employment versus all other employment status categories in a cohort of four middle-income countries (MIC). Of the effects of female sex on long COVID QALDs in Norway, UK, and the MIC cohort, 77% (46%,100%), 73% (52%, 94%), and 84% (62%, 100%) were unexplained by the clinical mediators, respectively. Our findings highlight that socio-economic proxies and sex may be as predictive of long COVID QALDs as commonly emphasized comorbidities and that broader structural determinants likely drive their impacts. Importantly, we outline a multi-method, adaptable causal machine learning approach for evaluating the isolated contributions of social disparities to long COVID quality of life experiences.

All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021) (preprint)

Official COVID-19 mortality statistics are strongly influenced by the local diagnostic capacity, strength of the healthcare system, and the recording and reporting capacities on causes of death. This can result in significant undercounting of COVID-19 attributable deaths, making it challenging to understand the total mortality burden of the pandemic. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock of the COVID-19 pandemic. Here, we use data from civil death registers for 54 municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to monthly data from January 2019 to February 2020, we estimate excess mortality over the course of the pandemic from March 2020 to April 2021. We estimated 16,000 [95% CI: 14,000, 18,000] excess deaths across these municipalities since March 2020. The sharpest increase in deaths was observed in April 2021, with an estimated 480% [95% CI: 390%, 580%] increase in mortality from expected counts for the same period. Females and the 40 to 60 age groups experienced a greater increase from baseline mortality compared to other demographic groups. Our excess mortality estimate for these 54 municipalities, representing approximately 5% of the state population, exceeds the official COVID-19 death count for the entire state of Gujarat.

Evaluating SARS-CoV-2 infection under tenofovir-based antiviral prophylaxis: a multi-scale modeling analysis upon experimental data (preprint)

Tenofovir has shown promising evidence of improving COVID-19 clinical outcomes in observational studies, still to be confirmed in clinical trials. Disease severity might be reduced under prophylaxis with the prodrug tenofovir disoproxil fumarate (TDF), while the protection seems to decrease, or even to lack, when using the alternative prodrug tenofovir alafenamide fumarate (TAF).  Aiming to understand why TDF-prophylaxis might reduce COVID-19 severity upon infection we developed a multi-scale analysis framework combining  in vitro  susceptibility data, molecular docking, and within-host dynamics modeling, and using remdesivir–the only antiviral approved to date against COVID-19– as a point of reference.First, our docking model predicted that intracellularly active tenofovir diphosphate binds into the SARS-CoV-2 RNA polymerase in the same site as the antiviral remdesivir triphosphate, but presents lower binding energy, likely reducing the overall inhibition of viral replication and making the antiviral efficacy more susceptible to the drug intracellular concentration. Second, using data from  in vitro  viral   cultures with plausible TDF therapeutic concentrations, we estimated that the drug can inhibit SARS-COV-2 replication at an efficacy ranging between 54-99%  conditional to the viral cycle length. Third, assuming values approximating this range of inhibition for  in vivo  viral replication during human SARS-COV-2 infection, we found that prophylaxis with TDF with high penetration into viral target cells is capable of delaying viral replication, mitigating direct cell damage and allowing time for the host to mount the adaptive immunity. Last, we found that the potential antiviral effect can be substantially reduced when TDF is given after infection begins. Our work provides a potential mechanistic explanation of the observed clinical effect of TDF against SARS-CoV-2 infection. The proposed inference framework can help to optimize the evaluation of antiviral therapies for COVID-19, in particular those targeting the RNA dependent RNA polymerase.

The relationship between human mobility measures and SAR-Cov-2 transmission varies by epidemic phase and urbanicity: results from the United States (preprint)

Global efforts to prevent the spread of the SARS-COV-2 pandemic in early 2020 focused on non-pharmaceutical interventions like social distancing; policies that aim to reduce transmission by changing mixing patterns between people. As countries have implemented these interventions, aggregated location data from mobile phones have become an important source of real-time information about human mobility and behavioral changes on a population level. Human activity measured using mobile phones reflects the aggregate behavior of a subset of people, and although metrics of mobility are related to contact patterns between people that spread the coronavirus, they do not provide a direct measure. In this study, we use results from a nowcasting approach from 1,396 counties across the US between January 22nd, 2020 and July 9th, 2020 to determine the effective reproductive number (R(t)) along an urban/rural gradient. For each county, we compare the time series of R(t) values with mobility proxies from mobile phone data from Camber Systems, an aggregator of mobility data from various providers in the United States. We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties compared to baseline, but that the relationship weakens considerably after the initial 15 weeks of the epidemic, consistent with the emergence of a more complex ecosystem of local policies and behaviors including masking. Importantly, we highlight potential issues in the data generation process, representativeness and equity of access which must be addressed to allow for general use of these data in public health.

Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile (preprint)

The current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality –measured either by direct COVID-19 attributed deaths or excess deaths– and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.

Genomic and mobility data reveal mass population movement as a driver of SARS-CoV-2 dissemination and diversity in Bangladesh (preprint)

BackgroundNew data streams are being used to track the pandemic of SARS-CoV-2, including genomic data which provides insights into patterns of importation and spatial spread of the virus, as well as population mobility data obtained from mobile phones. Here, we analyse the emergence and outbreak trajectory of SARS-CoV-2 in Bangladesh using these new data streams, and identify mass population movements as a key early event driving the ongoing epidemic. MethodsWe sequenced complete genomes of 67 SARS-CoV-2 samples (March-July 2020) and combined this dataset with 324 genomes from Bangladesh. For phylogenetic context, we also used 68,000 GISAID genomes collected globally. We paired this genomic data with population mobility information from Facebook and three mobile phone operators. FindingsThe majority (85%) of the Bangladeshi sequenced isolates fall into either pangolin lineage B.1.36 (8%), B.1.1 (19%) or B.1.1.25 (58%). Bayesian time-scaled phylogenetic analysis predicted SARS-COV-2 first appeared in mid-February, through international introductions. The first case was reported on March 8th. This pattern of repeated international introduction changed at the end of March when three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity across Bangladesh is reflected in the mobility data which shows the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka and the rest of the country during the following months. InterpretationIn Bangladesh, population mobility out of Dhaka as well as frequent travel from urban hotspots to rural areas resulted in rapid country-wide dissemination of SARS-CoV-2. The strains in Bangladesh reflect the local expansion of global lineages introduced early from international travellers to and from major international travel hubs. Importantly, the Bangladeshi context is consistent with epidemiologic and phylogenetic findings globally. Bangladesh is one of the few countries in the world with a rich history of conducting mass vaccination campaigns under complex circumstances. Combining genomics and these new data streams should allow population movements to be modelled and anticipated rendering Bangladesh extremely well prepared to immunize citizens rapidly. Based on our genomics data and the countrys successful immunization history, vaccines becoming available globally will be suitable for implementation in Bangladesh while ongoing genomic surveillance is conducted to monitor for new variants of the virus. FundingGovernment of Bangladesh, Bill and Melinda Gates Foundation, Wellcome Trust. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe emergence of SARS-CoV-2, leading to the COVID-19 pandemic, has motivated all countries in the world to obtain high resolution data on the virus. Globally over 300,000 strains have been sequenced and information made available in GISAID. Within the first 100 days of the emergence of SARS-CoV-2, genomic analysis from different countries led to the development of vaccines which have now reached market. Information on the prevailing genotypes of SARS-CoV-2 since introduction is needed in low and middle-income countries (LMICs), including Bangladesh, in order to determine the suitability of therapeutics and vaccines in the pipeline and help vaccine deployment. Added value of this studyWe sequenced SARS-CoV-2 genomes from strains that were prospectively collected during the height of the pandemic and combined these genomic data with mobility data to comprehensively describe i) how repeated international importations of SARS-CoV-2 were ultimately linked to nationwide spread, ii) 85% of strains belonged to the Pangolin lineages B.1.1, B.1.1.25 and B.1.36 and that similar mutation rates were observed as seen globally iii) the switch in genomic dynamics of SARS-CoV-2 coincided with mass migration out of cities to the rest of the country. We have assessed the contributions of population mobility on the maintenance and spread of clonal lineages of SARS-CoV-2. This is the first time these data types have been combined to look at the spread of this virus nationally. Implications of all the available evidenceSARS-CoV-2 genomic diversity and mutation rate in Bangladesh is comparable to strains circulating globally. Notably, the data on the genomic changes of SARS-CoV-2 in Bangladesh is reassuring, suggesting that immunotherapeutic and vaccines being developed globally should also be suitable for this population. Since Bangladesh already has extensive experience of conducting mass vaccination campaigns, such as the rollout of the oral Cholera vaccine, experience of developing and using new data streams will enable efficient and targeted immunization of the population in 2021 with COVID-19 vaccine(s).

Molecular dynamic simulation reveals E484K mutation enhances spike RBD-ACE2 affinity and the combination of E484K, K417N and N501Y mutations (501Y.V2 variant) induces conformational change greater than N501Y mutant alone, potentially resulting in an escape mutant (preprint)

Rapidly spreading SARS-CoV-2 variants present not only an increased threat to human health due to the confirmed greater transmissibility of several of these new strains but, due to conformational changes induced by the mutations, may render first-wave SARS-CoV-2 convalescent sera, vaccine-induced antibodies, or recombinant neutralizing antibodies (nAbs) ineffective. To be able to assess the risk of viral escape from neutralization by first-wave antibodies, we leveraged our capability for Molecular Dynamic (MD) simulation of the spike receptor binding domain (S RBD) and its binding to human angiotensin-converting enzyme 2 (hACE2) to predict alterations in molecular interactions resulting from the presence of the E484K, K417N, and N501Y variants found in the South African 501Y.V2 strain - alone and in combination. We report here the combination of E484K, K417N and N501Y results in the highest degree of conformational alterations of S RBD when bound to hACE2, compared to either E484K or N501Y alone. Both E484K and N501Y increase affinity of S RBD for hACE2 and E484K in particular switches the charge on the flexible loop region of RBD which leads to the formation of novel favorable contacts. Enhanced affinity of S RBD for hACE2 very likely underpins the greater transmissibility conferred by the presence of either E484K or N501Y; while the induction of conformational changes may provide an explanation for evidence that the 501Y.V2 variant, distinguished from the B.1.1.7 UK variant by the presence of E484K, is able to escape neutralization by existing first-wave anti-SARS-CoV-2 antibodies and re-infect COVID-19 convalescent individuals.

Emergence and Evolution of a Prevalent New SARS-CoV-2 Variant in the United States (preprint)

Genomic virus surveillance can lead to early identification of new variants and inform proper response during a pandemic. Using this approach, we have identified a new variant of the SARS-CoV-2 virus that emerged in the United States (U.S.) early in the coronavirus disease (COVID-19) pandemic and has become one of the most prevalent U.S variants. This new variant within the B.1.2 lineage referred to here as 20C-US, has not yet spread widely to other countries. The earliest 20C-US genomes can be traced to the southern U.S. in late May of 2020. A major early event was the rapid acquisition of five non-synonymous mutations. The changes carried by 20C-US include mutations to genes involved in virus particle maturation and release, processing of viral proteins, and RNA genome integrity and translation genes, all important for efficient and accurate virus production. In addition, 20C-US has since acquired two new non-synonymous mutations that highlight its ongoing evolution, one of which is a Q677H mutation in the spike protein adjacent to the furin cleavage site. We predict that 20C-US may already be the most dominant variant of SARS-CoV-2 in the U.S. The ongoing evolution of 20C-US, as well as other dominant region-specific variants emerging around the world, should continue to be monitored with genomic, epidemiologic, and experimental studies to understand viral evolution and predict future outcomes of the pandemic.

Fatal neuroinvasion of SARS-CoV-2 in K18-hACE2 mice is partially dependent on hACE2 expression (preprint)

Animal models recapitulating the distinctive features of severe COVID-19 are critical to enhance our understanding of SARS-CoV-2 pathogenesis. Transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) under the cytokeratin 18 promoter (K18-hACE2) represent a lethal model of SARS-CoV-2 infection. However, the cause(s) and mechanisms of lethality in this mouse model remain unclear. Here, we evaluated the spatiotemporal dynamics of SARS-CoV-2 infection for up to 14 days post-infection. Despite infection and moderate inflammation in the lungs, lethality was invariably associated with viral neuroinvasion and neuronal damage (including spinal motor neurons). Neuroinvasion occurred following virus transport through the olfactory neuroepithelium in a manner that was only partially dependent on hACE2. Interestingly, SARS-CoV-2 tropism was overall neither widespread among nor restricted to only ACE2-expressing cells. Although our work incites caution in the utility of the K18-hACE2 model to study global aspects of SARS-CoV-2 pathogenesis, it underscores this model as a unique platform for exploring the mechanisms of SARS-CoV-2 neuropathogenesis. SUMMARYCOVID-19 is a respiratory disease caused by SARS-CoV-2, a betacoronavirus. Here, we show that in a widely used transgenic mouse model of COVID-19, lethality is invariably associated with viral neuroinvasion and the ensuing neuronal disease, while lung inflammation remains moderate.

Using image-based haplotype alignments to map global adaptation of SARS-CoV-2 (preprint)

Quantifying evolutionary change among viral genomes is an important clinical device to track critical adaptations geographically and temporally. We built image-based haplotype-guided evolutionary inference (ImHapE) to quantify adaptations in expanding populations of non-recombining SARS-CoV-2 genomes. By combining classic population genetic summaries with image-based deep learning methods, we show that different rates of positive selection are driving evolutionary fitness and dispersal of SARS-CoV-2 globally. A 1.35-fold increase in evolutionary fitness is observed within the UK, associated with expansion of both the B.1.177 and B.1.1.7 SARS-CoV-2 lineages.

Targeting conserved viral virulence determinants by single domain antibodies to block SARS-CoV2 infectivity (preprint)

We selected SARS-CoV2 specific single domain antibodies (sdAbs) from a previously constructed phage display library using synthetic immunogenic peptides of the virus spike (S) protein as bait. The sdAbs targeting the cleavage site (CS) and the receptor binding domain (RBD) in S protein efficiently neutralised the infectivity of a pseudovirus expressing SARS-CoV2 S protein. Anti-CS sdAb blocked the virus infectivity by inhibiting proteolytic processing of SARS-CoV2 S protein. Both the sdAbs retained characteristic structure within the pH range of 2 to 12 and remained stable upto 65{degrees}C. Furthermore, structural disruptions induced by a high temperature in both the sdAbs were largely reversed upon their gradual cooling and the resulting products neutralised the reporter virus. Our results therefore suggest that targeting CS in addition to the RBD of S protein by sdAbs could serve as a viable option to reduce SARS-CoV2 infectivity and that proteolytic processing of the viral S protein is critical for infection.

In vitro screening of anti-viral and virucidal effects against SARS-CoV-2 by Hypericum perforatum and Echinacea. (preprint)

Special Infectious Agent Unit in King Fahd Medical Research Center at King Abdulaziz University, Jeddah, Saudi Arabia, has pursed the anti-viral project field to optimize the group of medicinal plants for human-infectious diseases. We have begun virtually in this field since COVID-19 pandemic, besides our divergence in the infectious agents. In this study and based on the previous review, Hypericum perforatum (St. Johns Wort) and Echinacea (gaia HERBS(R)) were tested in vitro using Vero E6 cells for their anti-viral effects against the newly identified Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) through its infectious cycle from 0 to 48 hours post infection. The hypericin (0.9 mg) of H. perforatum and the different parts (roots, seeds, aerial) of two types of Echinacea species (Echinacea purpurea and Echinacea angustifolia) were examined their efficacy in certain concentration and under light-dependent anti-viral activities to measure the inhibition of the SARS-CoV-2 mRNA expression of RNA-dependent RNA polymerase (RdRP) gene and the viral load with quantitative real-time polymerase chain reaction (qRT-PCR), and to assess the neutralization of the SARS-CoV-2 spike receptor binding on cell culture assay. Interestingly, the mixture (H.E.) of 100 mg/mL of H. perforatum and Echinacea was tested too on SARS-CoV-2 and showed crucial anti-viral activity competing H. perforatum then Echinacea effects as anti-viral treatment. Therefore, the results of gaia HERBS(R) products, H. perforatum and Echinacea species, applied in this study showed significant anti-viral and virucidal effects in the following order of potency: H. perforatum, H.E., and Echinacea on SARS-CoV-2 infectious cycle; and will definitely required a set up of clinical trial with specific therapeutic protocol based on the outcome of this study. Author SummaryAfter an outbreak of Rift Valley Fever in the Southern region of Saudi Arabia, particularly in May 2003, Special Infectious Agents Unit (SIAU) was established and founded by Prof. Esam Ibraheem Azhar. This unit contains a full range of facilities including Biosafety Level 3, allows him and his research groups to ambulate and culture risk group 3 viruses in Saudi Arabia & Gulf States for the first time. Since that time, SIAU and our international collaboration have been extended to implement a standard protocols in the infectious agents diagnostics procedure through different mode of collaboration including exchange of expertise, joint research program and more recently a technology transfer agreements with number of international institute sharing same interests. Furthermore, we have been engaged in number of researches related to Hajj & Umrah plus number of national services with the Ministry of Health (MOH) through which, we utilize our Mobile biosafety level 3 Lab to enhance the diagnostics of MERS CoV in the Holly sites during Hajj since 2014. In our SIAU and with a powerful team, we have excellent researches made valuable contributions through in vivo and in vitro animal and human studies, and several human viral pathogens which are a threat to global health security due to millions of pilgrims visiting Saudi Arabia every year from 182 countries: with particular areas of interests in: Alkhurma Viral Hemorrhagic Fever, Dengue Hemorrhagic Fever Viruses, Rift Valley Fever Virus, MERS-CoV and more recently the new global infectious diseases threat, Sever Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2).

An insight into neurotoxic and toxicity of spike fragments SARS-CoV-2 by exposure environment: A threat to aquatic health? (preprint)

The Spike protein (S protein) is a critical component in the infection of the new coronavirus (SARS-CoV-2). The objective of this work was to evaluate whether peptides from S protein could cause negative impact in the aquatic animals. The aquatic toxicity of SARS-CoV-2 spike protein peptides derivatives has been evaluated in tadpoles (n = 50 tadpoles / 5 replicates of 10 animals) from species Physalaemus cuvieri (Leptodactylidae). After synthesis, purification, and characterization of peptides (PSDP2001, PSDP2002, PSDP2003) an aquatic contamination has been simulatedwith these peptides during 24 hours of exposure in two concentrations (100 and 500 ng/mL). The control group ("C") was composed of tadpoles kept in polyethylene containers containing de-chlorinated water. Oxidative stress, antioxidant biomarkers and neurotoxicity activity were assessed. In both concentrations, PSPD2002 and PSPD2003 increased catalase and superoxide dismutase antioxidants enzymes activities, as well as oxidative stress (nitrite levels, hydrogen peroxide and reactive oxygen species). All three peptides also increased acetylcholinesterase activity in the highest concentration. These peptides showed molecular interactions in silico with acetylcholinesterase and antioxidant enzymes. Aquatic particle contamination of SARS-CoV-2 has neurotoxics effects in P. cuvieri tadpoles. These findings indicate that the COVID-19 can constitute environmental impact or biological damage potential. HIGHLIGHTSO_LISARS-CoV-2 spike protein peptides (PSDP) were synthesized, purified, and characterized by solid phase peptide synthesis. C_LIO_LIPSDP peptides promoted REDOX imbalance and acute neurotoxicity in tadpoles (Physalaemus cuvieri) C_LIO_LIIn silico studies have shown interactionsbetween peptides and acetylcholinesterase and antioxidant enzymes C_LIO_LIAquatic particle contamination of SARS-CoV-2 can constitute additional environmental damage C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/425914v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1b99c31org.highwire.dtl.DTLVardef@bd6d71org.highwire.dtl.DTLVardef@5c37f2org.highwire.dtl.DTLVardef@5d027d_HPS_FORMAT_FIGEXP M_FIG C_FIG

A rapid phenomics workflow for the in vitro identification of antiviral drugs (preprint)

Morphological profiling of cells in the presence of perturbants, also known as phenomics, is gaining momentum given its successful implementation for drug discovery and compound profiling. The current COVID-19 pandemic has fueled the search for new and fast methods to identify novel or repurposed therapeutic drugs. A popular method to identify antiviral drugs is the use of antibody-based immunofluorescence to visualise infected cells. However, this method lacks depth towards the effect of such drugs on the host cells. Here we present a phenomics workflow for untargeted phenotypic drug screening of virus infected cells, combining Cell Painting with antibody-based detection of viral infection in a single and simple method and provide a semi-automated image analysis pipeline for classification and feature extraction of virus infected cells. Our phenomics workflow provides valuable information about the effect of both virus and drugs on the host cells. We validated our method using a panel of 9 antiviral compounds including known and novel compounds on MRC5 human lung fibroblasts infected with Human coronavirus 229E (CoV-229E). Two of the compounds showed strong antiviral efficacy concomitant with a recovery of the morphological profile towards non-infected.

Phylogenetic analyses of SARS-CoV-2 B.1.1.7 lineage suggest a single origin followed by multiple exportation events versus convergent evolution (preprint)

The emergence of new variants of SARS-CoV-2 herald a new phase of the pandemic. This study used state-of-the-art phylodynamic methods to ascertain that the rapid rise of B.1.1.7 "Variant of Concern" most likely occurred by global dispersal rather than convergent evolution from multiple sources.

Lockdown related travel behavior undermines thecontainment of SARS-CoV-2 (preprint)

In response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public's response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

SARS-CoV-2 Transmission Dynamics Should Inform Policy (preprint)

It is generally agreed that striking a balance between resuming economic and social activities and keeping the effective reproductive number (R0) below 1 using non-pharmaceutical interventions is an important goal until and even after effective vaccines become available. Therefore, the need remains to understand how the virus is transmitted in order to identify high-risk environments and activities that disproportionately contribute to its spread so that effective preventative measures could be put in place. Contact tracing and household studies in particular provide robust evidence about the parameters of transmission. In this viewpoint, we discuss the available evidence from large-scale, well-conducted contact tracing studies from across the world and argue that SARS-CoV-2 transmission dynamics should inform policy decisions about mitigation strategies for targeted interventions according to the needs of the society by directing attention to the settings, activities and socioeconomic factors associated with the highest risks of transmission.

Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys (preprint)

Establishing how many people have already been infected by SARS-CoV-2 is an urgent priority for controlling the COVID-19 pandemic. Patchy virological testing has hampered interpretation of confirmed case counts, and unknown rates of asymptomatic and mild infections make it challenging to develop evidence-based public health policies. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies has been unclear. Here, we used a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across tested subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that serological positivity indicates immune protection, we propagated these estimates and uncertainty through dynamical models to assess the uncertainty in the epidemiological parameters needed to evaluate public health interventions. We examined the relative accuracy of convenience samples versus structured surveys to estimate population seroprevalence and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize the design of serological surveys given particular test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.

Estimating the number of undetected COVID-19 cases exported internationally from all of China (preprint)

Early in the COVID-19 pandemic, when cases were predominantly reported in the city of Wuhan, China, local outbreaks in Europe, North America, and Asia were largely predicted from imported cases on flights from Wuhan, potentially missing imports from other key source cities. Here, we account for importations from Wuhan and from other cities in China, combining COVID-19 prevalence estimates in 18 Chinese cities with estimates of flight passenger volume to predict for each day between early December 2019 to late February 2020 the number of cases exported from China. We predict that the main source of global case importation in early January was Wuhan, but due to the Wuhan lockdown and the rapid spread of the virus, the main source of case importation from mid February became Chinese cities outside of Wuhan. For destinations in Africa in particular, non-Wuhan cities were an important source of case imports (1 case from those cities for each case from Wuhan, range of model scenarios: 0.1-9.8). Our model predicts that 18.4 (8.5 - 100) COVID-19 cases were imported to 26 destination countries in Africa, with most of them (90%) predicted to have arrived between 7th January (+/-10 days) and 5th February (+/- 3 days), and all of them predicted prior to the first case detections. We finally observed marked heterogeneities in expected imported cases across those locations. Our estimates shed light on shifting sources and local risks of case importation which can help focus surveillance efforts and guide public health policy during the final stages of the pandemic. We further provide a time window for the seeding of local epidemics in African locations, a key parameter for estimating expected outbreak size and burden on local health care systems and societies, that has yet to be defined in these locations.