The mode and extent of rapid evolution and genomic change in response to human harvesting are key conservation issues. Although experiments and models have shown a high potential for both genetic and phenotypic change in response to fishing, empirical examples of genetic responses in wild populations are rare. Here, we compare whole-genome sequence data of Atlantic cod (Gadus morhua) that were collected before (early 20th century) and after (early 21st century) periods of intensive exploitation and rapid decline in the age of maturation from two geographically distinct populations in Newfoundland, Canada, and the northeast Arctic, Norway. Our temporal, genome-wide analyses of 346,290 loci show no substantial loss of genetic diversity and high effective population sizes. Moreover, we do not find distinct signals of strong selective sweeps anywhere in the genome, although we cannot rule out the possibility of highly polygenic evolution. Our observations suggest that phenotypic change in these populations is not constrained by irreversible loss of genomic variation and thus imply that former traits could be reestablished with demographic recovery.

Dengue is the most prevalent arboviral disease worldwide, and the four dengue virus (DENV) serotypes circulate endemically in many tropical and subtropical regions. Numerous studies have shown that the majority of DENV infections are inapparent, and that the ratio of inapparent to symptomatic infections (I/S) fluctuates substantially year-to-year. For example, in the ongoing Pediatric Dengue Cohort Study (PDCS) in Nicaragua, which was established in 2004, the I/S ratio has varied from 16.5:1 in 2006–2007 to 1.2:1 in 2009–2010. However, the mechanisms explaining these large fluctuations are not well understood. We hypothesized that in dengue-endemic areas, frequent boosting (i.e., exposures to DENV that do not lead to extensive viremia and result in a less than fourfold rise in antibody titers) of the immune response can be protective against symptomatic disease, and this can explain fluctuating I/S ratios. We formulate mechanistic epidemiologic models to examine the epidemiologic effects of protective homologous and heterologous boosting of the antibody response in preventing subsequent symptomatic DENV infection. We show that models that include frequent boosts that protect against symptomatic disease can recover the fluctuations in the I/S ratio that we observe, whereas a classic model without boosting cannot. Furthermore, we show that a boosting model can recover the inverse relationship between the number of symptomatic cases and the I/S ratio observed in the PDCS. These results highlight the importance of robust dengue control efforts, as intermediate dengue control may have the potential to decrease the protective effects of boosting.

Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: “close” (a small, unchanging group of mutual contacts as might be found in a household), “regular” (a larger, unchanging group as might be found in a workplace or school), and “random” (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k. We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles’ heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.

Epidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; P<3.2×10−3

The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered.

The investigation of migratory patterns during the SARS-CoV-2 pandemic before spring 2020 border closures in Europe is a crucial first step toward an in-depth evaluation of border closure policies. Here we analyze viral genome sequences using a phylodynamic model with geographic structure to estimate the origin and spread of SARS-CoV-2 in Europe prior to border closures. Based on SARS-CoV-2 genomes, we reconstruct a partial transmission tree of the early pandemic and coinfer the geographic location of ancestral lineages as well as the number of migration events into and between European regions. We find that the predominant lineage spreading in Europe during this time has a most recent common ancestor in Italy and was probably seeded by a transmission event in either Hubei, China or Germany. We do not find evidence for preferential migration paths from Hubei into different European regions or from each European region to the others. Sustained local transmission is first evident in Italy and then shortly thereafter in the other European regions considered. Before the first border closures in Europe, we estimate that the rate of occurrence of new cases from within-country transmission was within the bounds of the estimated rate of new cases from migration. In summary, our analysis offers a view on the early state of the epidemic in Europe and on migration patterns of the virus before border closures. This information will enable further study of the necessity and timeliness of border closures.

Rabies kills ∼60,000 people per year. Annual vaccination of at least 70% of dogs has been shown to eliminate rabies in both human and canine populations. However, delivery of large-scale mass dog vaccination campaigns remains a challenge in many rabies-endemic countries. In sub-Saharan Africa, where the vast majority of dogs are owned, mass vaccination campaigns have typically depended on a combination of static point (SP) and door-to-door (D2D) approaches since SP-only campaigns often fail to achieve 70% vaccination coverage. However, D2D approaches are expensive, labor-intensive, and logistically challenging, raising the need to develop approaches that increase attendance at SPs. Here, we report a real-time, data-driven approach to improve efficiency of an urban dog vaccination campaign. Historically, we vaccinated ∼35,000 dogs in Blantyre city, Malawi, every year over a 20-d period each year using combined fixed SP (FSP) and D2D approaches. To enhance cost effectiveness, we used our historical vaccination dataset to define the barriers to FSP attendance. Guided by these insights, we redesigned our vaccination campaign by increasing the number of FSPs and eliminating the expensive and labor-intensive D2D component. Combined with roaming SPs, whose locations were defined through the real-time analysis of vaccination coverage data, this approach resulted in the vaccination of near-identical numbers of dogs in only 11 d. This approach has the potential to act as a template for successful and sustainable future urban SP-only dog vaccination campaigns.