SARS-CoV-2 is transmitted primarily through close, person-to-person interactions. Physical distancing policies can control the spread of SARS-CoV-2 by reducing the amount of these interactions in a population. Here, we report results from four waves of contact surveys designed to quantify the impact of these policies during the COVID-19 pandemic in the United States. We surveyed 9,743 respondents between March 22 and September 26, 2020. We find that interpersonal contact has been dramatically reduced in the US, with an 82% (95%CI: 80%-83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. However, we find increases in contact rates over the subsequent waves. We also find that certain demographic groups, including people under 45 and males, have significantly higher contact rates than the rest of the population. Tracking these changes can provide rapid assessments of the impact of physical distancing policies and help to identify at-risk populations.

In the wake of the Rohingya population's mass migration from Myanmar, one of the world's largest refugee settlements was constructed in Cox's Bazar, Bangladesh to accommodate nearly 900,000 new refugees. Refugee populations are particularly vulnerable to infectious disease outbreaks due to many population and environmental factors. A large measles outbreak, with over 1700 cases, occurred among the Rohingya population between September and November 2017. Here, we estimate key epidemiological parameters and use a dynamic mathematical model of measles transmission to evaluate the effectiveness of the reactive vaccination campaigns in the refugee camps. We also estimate the potential for subsequent outbreaks under different vaccination coverage scenarios. Our modeling results highlight the success of the vaccination campaigns in rapidly curbing transmission and emphasize the public health importance of maintaining high levels of vaccination in this population, where high birth rates and historically low vaccination coverage rates create suitable conditions for future measles outbreaks.

Purpose of review

Vaccine-preventable diseases remain a major public health concern globally. Climate is a key driver of the dynamics of many infectious diseases, including those that are vaccine preventable. Understanding the impact of climate change on vaccine-preventable diseases is, thus, an important public health research priority. Here, we summarize the recent literature and highlight promising directions for future research.

Recent findings

Vaccine-preventable enteric diseases, such as cholera, exhibit sensitivity to precipitation and flooding events. The predicted increase in extreme weather events as a result of climate change could exacerbate outbreaks of these pathogens. For airborne pathogens, temperature and specific humidity have been shown to be the most important environmental drivers, although the impact of climate change on disease burden and dynamics remains unclear. Finally, the transmission dynamics of vector-borne diseases are dependent on both temperature and precipitation, and climate change is expected to alter the burden and geographic range of these diseases. However, understanding the interacting effects of multiple factors, including socioeconomic and ecological factors, on the vector-borne disease ecosystem will be a crucial step towards forecasting disease burden under climate change. Recent work has demonstrated associations between climate and transmission of vaccine-preventable diseases. Translating these findings into forecasts under various climate change scenarios will require mechanistic frameworks that account for both intrinsic and extrinsic drivers of transmission, and the non-linear effects on disease burden. Future research should also pay greater attention to uncertainty in both the climate modeling processes as well as disease outcomes in the context of vaccine-preventable diseases.

Rotavirus, a diarrheal pathogen spread via fecal-oral transmission, is typically characterized by a winter incidence peak in most countries. Unlike for cholera and other waterborne infections, the role of sanitation and socioeconomic factors on the spatial variation of rotavirus seasonality remains unclear. In the current study, we analyzed their association with rotavirus seasonality, specifically the odds of monsoon cases, across 46 locations from 2001 to 2012 in Dhaka. Drinking water from tube wells, compared to other sources, has a clear protective effect against cases during the monsoon, when flooding and water contamination are more likely. This finding supports a significant environmental component of transmission.

Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing coronavirus disease 2019 (COVID-19) pandemic globally. To augment traditional lab and hospital-based surveillance, Bangladesh established a participatory surveillance system for the public to self-report symptoms consistent with COVID-19 through multiple channels. Here, we report on the use of this system, which received over 3 million responses within two months, for tracking the COVID-19 outbreak in Bangladesh. Although we observe considerable noise in the data and initial volatility in the use of the different reporting mechanisms, the self-reported syndromic data exhibits a strong association with lab-confirmed cases at a local scale. Moreover, the syndromic data also suggests an earlier spread of the outbreak across Bangladesh than is evident from the confirmed case counts, consistent with predicted spread of the outbreak based on population mobility data. Our results highlight the usefulness of participatory syndromic surveillance for mapping disease burden generally, and particularly during the initial phases of an emerging outbreak.

A key question for infectious disease dynamics is the impact of the climate on future burden. Here, we evaluate the climate drivers of respiratory syncytial virus (RSV), an important determinant of disease in young children. We combine a dataset of county-level observations from the US with state-level observations from Mexico, spanning much of the global range of climatological conditions. Using a combination of nonlinear epidemic models with statistical techniques, we find consistent patterns of climate drivers at a continental scale explaining latitudinal differences in the dynamics and timing of local epidemics. Strikingly, estimated effects of precipitation and humidity on transmission mirror prior results for influenza. We couple our model with projections for future climate, to show that temperature-driven increases to humidity may lead to a northward shift in the dynamic patterns observed and that the likelihood of severe outbreaks of RSV hinges on projections for extreme rainfall.