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Jacob Lemieux, Katherine Siddle, and Bronwyn MacInnis of the lab of institute member Pardis Sabeti of ӳ����ý's Infectious Disease and Microbiome Program (IDMP) and Genomic Center for Infectious Diseases (GCID), and many colleagues used genomic epidemiology to track the introduction and spread of SARS-CoV-2 across the first wave of the epidemic in the Boston area. The team estimates that the virus entered the region at least 80 separate times, mostly from elsewhere in the US and from Europe. One of those introductions was linked to a superspreading event that led to massive community transmission across the city and to other states and countries. The team also investigated superspreading in other settings, including a single viral introduction into a nursing home that led to widespread transmission amongst residents and staff. A visual narrative summary of this work is available on , and blog posts from the Sabeti lab’s research associates and volunteer project managers from Stanley Center for Psychiatric Research about their contributions to the work.

Colin Worby from ӳ����ý's IDMP and GCID and collaborator Hsiao-Han Chang (National Tsing Hua University, Taiwan) used epidemic models to examine the role of face masks in mitigating the spread of COVID-19 in the general population. They found that face masks, even with a limited protective effect, can reduce infections and deaths, and delay the peak time of the epidemic. Learn more in the .

Andres Colubri, Pardis Sabeti, and collaborators at Harvard University and Brown University modeled the impact of university-level responses to past mumps outbreaks at Harvard in 2016 and at Ohio State University in 2014, and used the resulting insights to determine which control interventions are most effective. Their results suggest that universities should design more aggressive diagnostic procedures and stricter quarantine policies to reduce infectious disease incidence on their campuses.

Between April 9th and June 9th, 2020, the ӳ����ý’s COVID-19 diagnostic laboratory worked in partnership with the Massachusetts Department of Public Health to test 32,480 residents and staff from 366 skilled nursing facilities, nursing homes, and assisted living facilities across the state, regardless of whether these individuals showed any symptoms.

In addition to forming a critical part of the state’s effort to combat the spread of disease among vulnerable populations, this effort generated a large dataset worth exploring — nearly 33,000 tests, all processed at a single lab using the same standards, connected to data about the presence of symptoms and demographics. Initial findings were shared in this blog post, including that individuals with and without symptoms at the time of testing have similar viral loads, suggesting significant transmission regardless of current symptom status.

Initial reports suggest that COVID-19 disproportionately afflicts historically disadvantaged populations. Associate member Amit Khera of the Program in Medical and Population Genetics, Aniruddh Patel, Manish Paranjpe, and colleagues, investigated associations between race, socioeconomic factors, and COVID-19 hospitalizations among 418,794 participants of the UK Biobank, 549 of whom had been hospitalized. The team found that both Black and Asian participants had an increased risk of COVID-19 hospitalization compared to white participants. Measures of socioeconomic deprivation and household income tracked with risk as well, but accounting for such measures only modestly attenuated risks in Black participants. The researchers write that whether this increased risk relates to differences in biologic factors such as host genetics, pre-existing comorbidities, testing or hospitalization patterns, or additional disparities in social determinants of health warrants further study. 

Genomic data are a powerful new source of information about how viruses move through communities, and can help inform public health decisions. New data from the ӳ����ý’s viral genomics group — led by Jacob Lemieux and Bronwyn MacInnis, working in close partnership with colleagues from Massachusetts General Hospital and the Massachusetts Department of Public Health — provide insights into how SARS-CoV-2 entered and spread through the Boston area. The data suggest there were at least 30 independent introductions into the region from several international and domestic sources. It also sheds light on early superspreading events, including rapid and extensive transmission within a congregate living facility, and introduction and spread from an international conference that shaped the ongoing outbreak in Massachusetts.

Group testing, which involves combining samples from multiple individuals and testing as a single pool, may help expand diagnostic testing capacities. ӳ����ý Fellow Brian Cleary, associate member Michael Mina, core institute member Aviv Regev, and colleagues explored the effectiveness of group testing for SARS-CoV-2 using mathematical models of epidemic spread and viral kinetics. They identified designs that substantially increase the identification rate of infected individuals in resource-limited settings and accurately estimate overall prevalence with up to 400 times less testing, and confirmed their framework experimentally.

Testing wastewater for pathogens can complement estimates of an outbreak’s intensity and provide data for epidemiologic modeling. Core institute member Eric Alm and colleagues sampled wastewater from a Greater Boston-area treatment plant and found higher levels of SARS-CoV-2 RNA than would be expected based on confirmed COVID-19 case counts.

led by Alm, the presence of SARS-CoV-2 RNA in wastewater from a Massachusetts facility increased exponentially from mid-March to mid-April 2020 and then declined, preceding a peak in clinical cases by 4-10 days. Monitoring wastewater may help identify early trends in disease transmission, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.