Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases.

The COVID-19 outbreak is a global pandemic with community circulation in many countries, including the United States, with confirmed cases in all states. The course of this pandemic will be shaped by how governments enact timely policies and disseminate information and by how the public reacts to policies and information. Here, we examine information-seeking responses to the first COVID-19 case public announcement in a state. Using an event study framework for all US states, we show that such news increases collective attention to the crisis right away. However, the elevated level of attention is short-lived, even though the initial announcements are followed by increasingly strong policy measures. Specifically, searches for "coronavirus" increased by about 36% (95% CI: 27 to 44%) on the day immediately after the first case announcement but decreased back to the baseline level in less than a week or two. We find that people respond to the first report of COVID-19 in their state by immediately seeking information about COVID-19, as measured by searches for coronavirus, coronavirus symptoms, and hand sanitizer. On the other hand, searches for information regarding community-level policies (e.g., quarantine, school closures, testing) or personal health strategies (e.g., masks, grocery delivery, over-the-counter medications) do not appear to be immediately triggered by first reports. These results are representative of the study period being relatively early in the epidemic, and more-elaborate policy responses were not yet part of the public discourse. Further analysis should track evolving patterns of responses to subsequent flows of public information.

Estimation of timing of infection from longitudinal SARS-CoV-2 viral load data: mathematical modelling study.

BACKGROUND: Multiple waves of the COVID-19 epidemic have hit most countries by the end of 2021. Most of those waves are caused by emergence and importation of new variants. To prevent importation of new variants, combination of border control and contact tracing is essential. However, the timing of infection inferred by interview is influenced by recall bias and hinders the contact tracing process. METHODS: We propose a novel approach to infer the timing of infection, by employing a within-host model to capture viral load dynamics after the onset of symptoms. We applied this approach to ascertain secondary transmission which can trigger outbreaks. As a demonstration, the 12 initial reported cases in Singapore, which were considered as imported because of their recent travel history to Wuhan, were analyzed to assess whether they are truly imported. RESULTS: Our approach suggested that 6 cases were infected prior to the arrival in Singapore, whereas other 6 cases might have been secondary local infection. Three among the 6 potential secondary transmission cases revealed that they had contact history to previously confirmed cases. CONCLUSIONS: Contact trace combined with our approach using viral load data could be the key to mitigate the risk of importation of new variants by identifying cases as early as possible and inferring the timing of infection with high accuracy.

Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data.

Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4) for Alpha and 4.5 days (95% CrI 4.0-5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27-8.44) for Alpha and of 6.52 days (95% CrI 5.54-8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29-2.58) for Alpha and 2.74 days (95% CrI 2.62-2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48-51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions.

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R0 at 2.4 (95% CI: 1.6-3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31-68 days), causing 9,216 more cases (95% CI: 1,317-25,545).

Characterizing all-cause excess mortality patterns during COVID-19 pandemic in Mexico.

BACKGROUND: Low testing rates and delays in reporting hinder the estimation of the mortality burden associated with the COVID-19 pandemic. During a public health emergency, estimating all cause excess deaths above an expected level of death can provide a more reliable picture of the mortality burden. Here, we aim to estimate the absolute and relative mortality impact of COVID-19 pandemic in Mexico. METHODS: We obtained weekly mortality time series due to all causes for Mexico, and by gender, and geographic region from 2015 to 2020. We also compiled surveillance data on COVID-19 cases and deaths to assess the timing and intensity of the pandemic and assembled weekly series of the proportion of tweets about 'death' from Mexico to assess the correlation between people's media interaction about 'death' and the rise in pandemic deaths. We estimated all-cause excess mortality rates and mortality rate ratio increase over baseline by fitting Serfling regression models and forecasted the total excess deaths for Mexico for the first 4 weeks of 2021 using the generalized logistic growth model. RESULTS: We estimated the all-cause excess mortality rate associated with the COVID-19 pandemic in Mexico in 2020 at 26.10 per 10,000 population, which corresponds to 333,538 excess deaths. Males had about 2-fold higher excess mortality rate (33.99) compared to females (18.53). Mexico City reported the highest excess death rate (63.54) and RR (2.09) compared to rest of the country (excess rate = 23.25, RR = 1.62). While COVID-19 deaths accounted for only 38.64% of total excess deaths in Mexico, our forecast estimate that Mexico has accumulated a total of ~ 61,610 [95% PI: 60,003, 63,216] excess deaths in the first 4 weeks of 2021. Proportion of tweets was significantly correlated with the excess mortality (ρ = 0.508 [95% CI: 0.245, 0.701], p-value = 0.0004). CONCLUSION: The COVID-19 pandemic has heavily affected Mexico. The lab-confirmed COVID-19 deaths accounted for only 38.64% of total all cause excess deaths (333,538) in Mexico in 2020. This reflects either the effect of low testing rates in Mexico, or the surge in number of deaths due to other causes during the pandemic. A model-based forecast indicates that an average of 61,610 excess deaths have occurred in January 2021.

Impacts of state COVID-19 reopening policy on human mobility and mixing behavior.

This study quantifies the effect of the 2020 state COVID economic activity reopening policies on daily mobility and mixing behavior, adding to the economic literature on individual responses to public health policy that addresses public contagion risks. We harness cellular device signal data and the timing of reopening plans to provide an assessment of the extent to which human mobility and physical proximity in the United States respond to the reversal of state closure policies. We observe substantial increases in mixing activities, 13.56% at 4 days and 48.65% at 4 weeks, following reopening events. Echoing a theme from the literature on the 2020 closures, mobility outside the home increased on average prior to these state actions. Furthermore, the largest increases in mobility occurred in states that were early adopters of closure measures and hard-hit by the pandemic, suggesting that psychological fatigue is an important barrier to implementation of closure policies extending for prolonged periods of time.

Maternal pertussis immunisation: clinical gains and epidemiological legacy.

The increase in whooping cough (pertussis) incidence in many countries with high routine vaccination coverage is alarming, with incidence in the US reaching almost 50,000 reported cases per year, reflecting incidence levels not seen since the 1950s. While the potential explanations for this resurgence remain debated, we face an urgent need to protect newborns, especially during the time window between birth and the first routine vaccination dose. Maternal immunisation has been proposed as an effective strategy for protecting neonates, who are at higher risk of severe pertussis disease and mortality. However, if maternally derived antibodies adversely affect the immunogenicity of the routine schedule, through blunting effects, we may observe a gradual degradation of herd immunity. 'Wasted' vaccines would result in an accumulation of susceptible children in the population, specifically leading to an overall increase in incidence in older age groups. In this Perspective, we discuss potential long-term epidemiological effects of maternal immunisation, as determined by possible immune interference outcomes.

Forecasting Epidemiological Consequences of Maternal Immunization.

BACKGROUND: The increase in the incidence of whooping cough (pertussis) in many countries with high vaccination coverage is alarming. Maternal pertussis immunization has been proposed as an effective means of protecting newborns during the interval between birth and the first routine dose. However, there are concerns regarding potential interference between maternal antibodies and the immune response elicited by the routine schedule, with possible long-term population-level effects. METHODS: We formulated a transmission model comprising both primary routine and maternal immunization. This model was examined to evaluate the long-term epidemiological effects of routine and maternal immunization, together with consequences of potential immune interference scenarios. RESULTS: Overall, our model demonstrates that maternal immunization is an effective strategy in reducing the incidence of pertussis in neonates prior to the onset of the primary schedule. However, if maternal antibodies lead to blunting, incidence increases among older age groups. For instance, our model predicts that with 60% routine and maternal immunization coverage and 30% blunting, the incidence among neonates (0-2 months) is reduced by 43%. Under the same scenario, we observe a 20% increase in incidence among children aged 5-10 years. However, the downstream increase in the older age groups occurs with a delay of approximately a decade or more. CONCLUSIONS: Maternal immunization has clear positive effects on infant burden of disease, lowering mean infant incidence. However, if maternally derived antibodies adversely affect the immunogenicity of the routine schedule, we predict eventual population-level repercussions that may lead to an overall increase in incidence in older age groups.

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

Genomic and eco-epidemiological investigations in Uruguay reveal local Chikungunya virus transmission dynamics during its expansion across the Americas in 2023.

Uruguay experienced its first Chikungunya virus outbreak in 2023, resulting in a significant burden to its healthcare system. We conducted analysis based on real-time genomic surveillance (30 novel whole genomes) to offer timely insights into recent local transmission dynamics and eco-epidemiological factors behind its emergence and spread in the country.

Toward a global virus genomic surveillance network.

The COVID-19 pandemic galvanized the field of virus genomic surveillance, demonstrating its utility for public health. Now, we must harness the momentum that led to increased infrastructure, training, and political will to build a sustainable global genomic surveillance network for other epidemic and endemic viruses. We suggest a generalizable modular sequencing framework wherein users can easily switch between virus targets to maximize cost-effectiveness and maintain readiness for new threats. We also highlight challenges associated with genomic surveillance and when global inequalities persist. We propose solutions to mitigate some of these issues, including training and multilateral partnerships. Exploring alternatives to clinical sequencing can also reduce the cost of surveillance programs. Finally, we discuss how establishing genomic surveillance would aid control programs and potentially provide a warning system for outbreaks, using a global respiratory virus (RSV), an arbovirus (dengue virus), and a regional zoonotic virus (Lassa virus) as examples.

Genomic and eco-epidemiological investigations in Uruguay reveal local Chikungunya virus transmission dynamics during its expansion across the Americas in 2023.

Uruguay experienced its first Chikungunya virus outbreak in 2023, resulting in a significant burden to its healthcare system. We conducted analysis based on real-time genomic surveillance (30 novel whole genomes) to offer timely insights into recent local transmission dynamics and eco-epidemiological factors behind its emergence and spread in the country.

Genomics-based timely detection of Dengue Virus type I genotypes I and V in Uruguay.

We report the first whole-genome sequences of Dengue Virus type I genotypes I and V from Uruguay, including the first cases ever reported in the country. Through timely genomic analysis, identification of these genotypes was possible, aiding in timely public health responses and intervention strategies to mitigate the impact of dengue outbreaks.

Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil.

Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil, our understanding of disease outbreaks is hampered by limited viral genomic data. Here, through a combination of phylogenetic and epidemiological models, we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated Aedes aegypti was able to capture the seasonality of reported human infections. Spatial modeling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centers in the Southeast. Phylodynamic analysis unraveled the circulation of three distinct lineages and provided proof of the directionality of a known spatial corridor that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control.

Disease-economy trade-offs under alternative epidemic control strategies.

Public policy and academic debates regarding pandemic control strategies note disease-economy trade-offs, often prioritizing one outcome over the other. Using a calibrated, coupled epi-economic model of individual behavior embedded within the broader economy during a novel epidemic, we show that targeted isolation strategies can avert up to 91% of economic losses relative to voluntary isolation strategies. Unlike widely-used blanket lockdowns, economic savings of targeted isolation do not impose additional disease burdens, avoiding disease-economy trade-offs. Targeted isolation achieves this by addressing the fundamental coordination failure between infectious and susceptible individuals that drives the recession. Importantly, we show testing and compliance frictions can erode some of the gains from targeted isolation, but improving test quality unlocks the majority of the benefits of targeted isolation.

College openings in the United States increase mobility and COVID-19 incidence.

School and college reopening-closure policies are considered one of the most promising non-pharmaceutical interventions for mitigating infectious diseases. Nonetheless, the effectiveness of these policies is still debated, largely due to the lack of empirical evidence on behavior during implementation. We examined U.S. college reopenings' association with changes in human mobility within campuses and in COVID-19 incidence in the counties of the campuses over a twenty-week period around college reopenings in the Fall of 2020. We used an integrative framework, with a difference-in-differences design comparing areas with a college campus, before and after reopening, to areas without a campus and a Bayesian approach to estimate the daily reproductive number (Rt). We found that college reopenings were associated with increased campus mobility, and increased COVID-19 incidence by 4.9 cases per 100,000 (95% confidence interval [CI]: 2.9-6.9), or a 37% increase relative to the pre-period mean. This reflected our estimate of increased transmission locally after reopening. A greater increase in county COVID-19 incidence resulted from campuses that drew students from counties with high COVID-19 incidence in the weeks before reopening (χ2(2) = 8.9, p = 0.012) and those with a greater share of college students, relative to population (χ2(2) = 98.83, p < 0.001). Even by Fall of 2022, large shares of populations remained unvaccinated, increasing the relevance of understanding non-pharmaceutical decisions over an extended period of a pandemic. Our study sheds light on movement and social mixing patterns during the closure-reopening of colleges during a public health threat, and offers strategic instruments for benefit-cost analyses of school reopening/closure policies.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Designing isolation guidelines for COVID-19 patients with rapid antigen tests.

Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adopted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation, whereas others may still be infectious. Utilizing viral test results to determine isolation length would minimize both the risk of prematurely ending isolation of infectious patients and the unnecessary individual burden of redundant isolation of noninfectious patients. In this study, we develop a data-driven computational framework to compute the population-level risk and the burden of different isolation guidelines with rapid antigen tests (i.e., lateral flow tests). Here, we show that when the detection limit is higher than the infectiousness threshold values, additional consecutive negative results are needed to ascertain infectiousness status. Further, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation.

Designing isolation guidelines for COVID-19 patients utilizing rapid antigen tests: a simulation study using viral dynamics models.

Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adapted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation (thus they are redundantly isolated), whereas others may still be infectious. Utilizing viral test results to determine ending isolation would minimize both the risk of ending isolation of infectious patients and the burden due to redundant isolation of noninfectious patients. In our previous study, we proposed a computational framework using SARS-CoV-2 viral dynamics models to compute the risk and the burden of different isolation guidelines with PCR tests. In this study, we extend the computational framework to design isolation guidelines for COVID-19 patients utilizing rapid antigen tests. Time interval of tests and number of consecutive negative tests to minimize the risk and the burden of isolation were explored. Furthermore, the approach was extended for asymptomatic cases. We found the guideline should be designed considering various factors: the infectiousness threshold values, the detection limit of antigen tests, symptom presence, and an acceptable level of releasing infectious patients. Especially, when detection limit is higher than the infectiousness threshold values, more consecutive negative results are needed to ascertain loss of infectiousness. To control the risk of releasing of infectious individuals under certain levels, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation, and the length of prolonged isolation increases when the detection limit is higher than the infectiousness threshold values, even though the guidelines are optimized for given conditions.