Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol.

Background: Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods: To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion: Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021.

Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework.

OBJECTIVES: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). METHODS: A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. RESULTS: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. CONCLUSIONS: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

A repeated cross-sectional analysis on the economic impact of SARS-CoV-2 pandemic at the hospital level in Italy.

Italy was the first country in Europe to be hit by the Severe Acute Respiratory Syndrome Coronavirus 2. Little research has been conducted to understand the economic impact of providing care for SARS-CoV-2 patients during the pandemic. Our study aims to quantify the incremental healthcare costs for hospitalizations associated to being discharged before or after the first SARS-CoV-2 case was notified in Italy, and to a positive or negative SARS-CoV-2 notified infection. We used data on hospitalizations for 9 different diagnosis related groups at a large Italian Research Hospital with discharge date between 1st January, 2018 and 31st December 2021. The median overall costs for a hospitalization increased from 2410EUR (IQR: 1588-3828) before the start of the pandemic, to 2645EUR (IQR: 1885-4028) and 3834EUR (IQR: 2463-6413) during the pandemic, respectively for patients SARS-CoV-2 negative and positive patients. Interestingly, according to results of a generalized linear model, the highest increases in the average costs sustained for SARS-CoV-2 positive patients with respect to patients discharged before the pandemic was found among those with diagnoses unrelated to COVID-19, i.e. kidney and urinary tract infections with CC (59.71%), intracranial hemorrhage or cerebral infarction (53.33), and pulmonary edema and respiratory failure (47.47%). Our study highlights the economic burden during the COVID-19 pandemic on the hospital system in Italy based on individual patient data. These results contribute to the to the debate around the efficiency of the healthcare services provision during a pandemic.

Using mortuary and burial data to place COVID-19 in Lusaka, Zambia within a global context.

Reported COVID-19 cases and associated mortality remain low in many sub-Saharan countries relative to global averages, but true impact is difficult to estimate given limitations around surveillance and mortality registration. In Lusaka, Zambia, burial registration and SARS-CoV-2 prevalence data during 2020 allow estimation of excess mortality and transmission. Relative to pre-pandemic patterns, we estimate age-dependent mortality increases, totalling 3212 excess deaths (95% CrI: 2104-4591), representing an 18.5% (95% CrI: 13.0-25.2%) increase relative to pre-pandemic levels. Using a dynamical model-based inferential framework, we find that these mortality patterns and SARS-CoV-2 prevalence data are in agreement with established COVID-19 severity estimates. Our results support hypotheses that COVID-19 impact in Lusaka during 2020 was consistent with COVID-19 epidemics elsewhere, without requiring exceptional explanations for low reported figures. For more equitable decision-making during future pandemics, barriers to ascertaining attributable mortality in low-income settings must be addressed and factored into discourse around reported impact differences.

Personal risk or societal benefit? Investigating adults' support for COVID-19 childhood vaccination.

Parental hesitancy poses a serious threat to the success of the COVID-19 childhood vaccination campaign. We investigate whether adults' opinions on childhood vaccination can be influenced via two survey experiments in Italy (n = 3,633 participants) and the UK (n = 3,314 participants). Respondents were randomly assigned to: a "risk treatment" that highlighted the potential risks of COVID-19 to a child, a "herd immunity treatment" that emphasized the community benefits of pediatric vaccination, or a control message. Participants' probability of supporting COVID-19 childhood vaccination was then assessed on a 0-100 scale. We find that the "risk treatment" reduced the proportion of Italian parents strongly against vaccination by up to 29.6 %, while increasing the proportion of neutral parents by up to 45.0 %. The "herd immunity treatment", instead, was only effective among non-parents, resulting in a lower proportion of individuals against pediatric vaccination and a higher proportion of individuals in favor (both shifted by around 20 %).

Factors influencing the decision to receive seasonal influenza vaccination among US corporate non-healthcare workers.

Influenza causes significant mortality and morbidity in the United States (US). Employees are exposed to influenza at work and can spread it to others. The influenza vaccine is safe, effective, and prevents severe outcomes; however, coverage among US adults (50.2%) is below Healthy People 2030 target of 70%. These highlights need for more effective vaccination promotion interventions. Understanding predictors of vaccination acceptance could inform vaccine promotion messages, improve coverage, and reduce illness-related work absences. We aimed to identify factors influencing influenza vaccination among US non-healthcare workers. Using mixed-methods approach, we evaluated factors influencing influenza vaccination among employees in three US companies during April-June 2020. Survey questions were adapted from the WHO seasonal influenza survey. Most respondents (n = 454) were women (272, 59.9%), 20-39 years old (n = 250, 55.1%); white (n = 254, 56.0%); had a college degree (n = 431, 95.0%); and reported receiving influenza vaccine in preceding influenza season (n = 297, 65.4%). Logistic regression model was statistically significant, X (16, N = 450) = 31.6, p = .01. Education [(OR) = 0.3, 95%CI = 0.1-0.6)] and race (OR = 0.4, 95%CI = 0.2-0.8) were significant predictors of influenza vaccine acceptance among participants. The majority had favorable attitudes toward influenza vaccination and reported that physician recommendation would influence their vaccination decisions. Seven themes were identified in qualitative analysis: "Protecting others" (109, 24.0%), "Protecting self" (105, 23.1%), "Vaccine accessibility" (94, 20.7%), "Education/messaging" (71, 15.6%), "Policies/requirements" (15, 3.3%), "Reminders" (9, 2.0%), and "Incentives" (3, 0.7%). Our findings could facilitate the development of effective influenza vaccination promotion messages and programs for employers, and workplace vaccination programs for other diseases such as COVID-19, by public health authorities.

Influenza causes significant mortality and morbidity in the United States (US).The US working-age group (18­64-year-old) bears a huge burden of influenza annually.Influenza vaccination coverage in the working-age group is low.Physicians and employers can influence vaccine acceptance of working adults.Employers can consider practical steps, e.g., incentivizing, or offering vaccine onsite.

Investigating the relationship between interventions, contact patterns, and SARS-CoV-2 transmissibility.

BACKGROUND: After a rapid upsurge of COVID-19 cases in Italy during the fall of 2020, the government introduced a three-tiered restriction system aimed at increasing physical distancing. The Ministry of Health, after periodic epidemiological risk assessments, assigned a tier to each of the 21 Italian regions and autonomous provinces. It is still unclear to what extent these different sets of measures altered the number of daily interactions and the social mixing patterns. METHODS AND FINDINGS: We conducted a survey between July 2020 and March 2021 to monitor changes in social contact patterns among individuals in the metropolitan city of Milan, Italy, which was hardly hit by the second wave of the COVID-19 pandemic. The number of daily contacts during periods characterized by different levels of restrictions was analyzed through negative binomial regression models and age-specific contact matrices were estimated under the different tiers of restrictions. By relying on the empirically estimated mixing patterns, we quantified relative changes in SARS-CoV-2 transmission potential associated with the different tiers. As tighter restrictions were implemented during the fall of 2020, a progressive reduction in the mean number of daily contacts recorded by study participants was observed: from 15.9 % under mild restrictions (yellow tier), to 41.8 % under strong restrictions (red tier). Higher restrictions levels were also found to increase the relative contribution of contacts occurring within the household. The SARS-CoV-2 reproduction number was estimated to decrease by 17.1 % (95 %CI: 1.5-30.1), 25.1 % (95 %CI: 13.0-36.0) and 44.7 % (95 %CI: 33.9-53.0) under the yellow, orange, and red tiers, respectively. CONCLUSIONS: Our results give an important quantification of the expected contribution of different restriction levels in shaping social contacts and decreasing the transmission potential of SARS-CoV-2. These estimates can find an operational use in anticipating the effect that the implementation of these tiered restriction can have on SARS-CoV-2 reproduction number under an evolving epidemiological situation.

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021.

Non-pharmaceutical interventions minimize social contacts, hence the spread of SARS-CoV-2. We quantified two-day contact patterns among USA employees from 2020-2021 during the COVID-19 pandemic. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic diaries. Mean (standard deviation) contacts reported by 1,456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We modeled SARS-CoV-2 transmission at home, work, and community. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained very low in work settings. Contact data are important to parameterize models of infection transmission and control. Teaser: Changes in social contact patterns shape disease dynamics at workplaces in the USA.

Altruism and vaccination intentions: Evidence from behavioral experiments.

Vaccine hesitancy has been on the rise throughout the past two decades, especially in high income countries where existing pro-vaccination public health communication strategies have proven ineffective. We argue that appealing to other-regarding preferences is one way of improving the effectiveness of public health communication strategies. To test this argument, we assess how vaccination intentions are influenced by the presence of people who cannot vaccinate, such as the immunosuppressed, newborns or pregnant women, using a laboratory experiment where there is a passive player whose welfare depends on the decisions of other, active players. Results suggest that pro-vaccine messages targeting altruism can increase vaccination intentions by: (i) invoking past experiences of dependence and vulnerability; (ii) stressing cooperation as a social norm; and (iii) emphasizing the presence of vulnerable individuals in a given society.

Pressure on the Health-Care System and Intensive Care Utilization During the COVID-19 Outbreak in the Lombardy Region of Italy: A Retrospective Observational Study in 43,538 Hospitalized Patients.

During the spring of 2020, the coronavirus disease 2019 (COVID-19) epidemic caused an unprecedented demand for intensive-care resources in the Lombardy region of Italy. Using data on 43,538 hospitalized patients admitted between February 21 and July 12, 2020, we evaluated variations in intensive care unit (ICU) admissions and mortality over the course of 3 periods: the early phase of the pandemic (February 21-March 13), the period of highest pressure on the health-care system (March 14-April 25, when numbers of COVID-19 patients exceeded prepandemic ICU bed capacity), and the declining phase (April 26-July 12). Compared with the early phase, patients aged 70 years or more were less often admitted to an ICU during the period of highest pressure on the health-care system (odds ratio (OR) = 0.47, 95% confidence interval (CI): 0.41, 0.54), with longer ICU delays (incidence rate ratio = 1.82, 95% CI: 1.52, 2.18) and lower chances of dying in the ICU (OR = 0.47, 95% CI: 0.34, 0.64). Patients under 56 years of age had more limited changes in the probability of (OR = 0.65, 95% CI: 0.56, 0.76) and delay to (incidence rate ratio = 1.16, 95% CI: 0.95, 1.42) ICU admission and increased mortality (OR = 1.43, 95% CI: 1.00, 2.07). In the declining phase, all quantities decreased for all age groups. These patterns may suggest that limited health-care resources during the peak phase of the epidemic in Lombardy forced a shift in ICU admission criteria to prioritize patients with higher chances of survival.

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden.

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95%CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3-21) days; the length of stay in ICU was 11 (IQR: 6-19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.

Social contact patterns and implications for infectious disease transmission - a systematic review and meta-analysis of contact surveys.

Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings. Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings. Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made. Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions. Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1).

Infectious diseases, particularly those caused by airborne pathogens like SARS-CoV-2, spread by social contact, and understanding how people mix is critical in controlling outbreaks. To explore these patterns, researchers typically carry out large contact surveys. Participants are asked for personal information (such as gender, age and occupation), as well as details of recent social contacts, usually those that happened in the last 24 hours. This information includes, the age and gender of the contact, where the interaction happened, how long it lasted, and whether it involved physical touch. These kinds of surveys help scientists to predict how infectious diseases might spread. But there is a problem: most of the data come from high-income countries, and there is evidence to suggest that social contact patterns differ between places. Therefore, data from these countries might not be useful for predicting how infections spread in lower-income regions. Here, Mousa et al. have collected and combined data from 27 contact surveys carried out before the COVID-19 pandemic to see how baseline social interactions vary between high- and lower-income settings. The comparison revealed that, in higher-income countries, the number of daily contacts people made decreased with age. But, in lower-income countries, younger and older individuals made similar numbers of contacts and mixed with all age groups. In higher-income countries, more contacts happened at work or school, while in low-income settings, more interactions happened at home and people were also more likely to live in larger, intergenerational households. Mousa et al. also found that gender affected how long contacts lasted and whether they involved physical contact, both of which are key risk factors for transmitting airborne pathogens. These findings can help researchers to predict how infectious diseases might spread in different settings. They can also be used to assess how effective non-medical restrictions, like shielding of the elderly and workplace closures, will be at reducing transmissions in different parts of the world.

The early phase of the COVID-19 epidemic in Lombardy, Italy.

BACKGROUND: In the night of February 20, 2020, the first epidemic of the novel coronavirus disease (COVID-19) outside Asia was uncovered by the identification of its first patient in Lombardy region, Italy. In the following weeks, Lombardy experienced a sudden increase in the number of ascertained infections and strict measures were imposed to contain the epidemic spread. METHODS: We analyzed official records of cases occurred in Lombardy to characterize the epidemiology of SARS-CoV-2 during the early phase of the outbreak. A line list of laboratory-confirmed cases was set up and later retrospectively consolidated, using standardized interviews to ascertained cases and their close contacts. We provide estimates of the serial interval, of the basic reproduction number, and of the temporal variation of the net reproduction number of SARS-CoV-2. RESULTS: Epidemiological investigations detected over 500 cases (median age: 69, IQR: 57-78) before the first COVID-19 diagnosed patient (February 20, 2020), and suggested that SARS-CoV-2 was already circulating in at least 222 out of 1506 (14.7%) municipalities with sustained transmission across all the Lombardy provinces. We estimated the mean serial interval to be 6.6 days (95% CrI, 0.7-19). Our estimates of the basic reproduction number range from 2.6 in Pavia (95% CI, 2.1-3.2) to 3.3 in Milan (95% CI, 2.9-3.8). A decreasing trend in the net reproduction number was observed following the detection of the first case. CONCLUSIONS: At the time of first case notification, COVID-19 was already widespread in the entire Lombardy region. This may explain the large number of critical cases experienced by this region in a very short timeframe. The slight decrease of the reproduction number observed in the early days after February 20, 2020 might be due to increased population awareness and early interventions implemented before the regional lockdown imposed on March 8, 2020.

Individual's daily behaviour and intergenerational mixing in different social contexts of Kenya.

We investigated contact patterns in diverse social contexts in Kenya and the daily behaviours that may play a pivotal role in infection transmission to the most vulnerable leveraging novel data from a 2-day survey on social contacts and time use (TU) from a sample of 1407 individuals (for a total of 2705 person days) from rural, urban formal, and informal settings. We used TU data to build six profiles of daily behaviour based on the main reported activities, i.e., Homestayers (71.1% of person days), Workers (9.3%), Schoolers (7.8%), or locations at increasing distance from home, i.e., Walkers (6.6%), Commuters (4.6%), Travelers (0.6%). In the rural setting, we observed higher daily contact numbers (11.56, SD 0.23) and percentages of intergenerational mixing with older adults (7.5% of contacts reported by those younger than 60 years vs. less than 4% in the urban settings). Overall, intergenerational mixing with older adults was higher for Walkers (7.3% of their reported contacts), Commuters (8.7%), and Homestayers (5.1%) than for Workers (1.5%) or Schoolers (3.6%). These results could be instrumental in defining effective interventions that acknowledge the heterogeneity in social contexts and daily routines, either in Kenya or other demographically and culturally similar sub-Saharan African settings.

Experimental evidence that changing beliefs about mask efficacy and social norms increase mask wearing for COVID-19 risk reduction: Results from the United States and Italy.

In the absence of widespread vaccination for COVID-19, governments and public health officials have advocated for the public to wear masks during the pandemic. The decision to wear a mask in public is likely affected by both beliefs about its efficacy and the prevalence of the behavior. Greater mask use in the community may encourage others to follow this norm, but it also creates an incentive for individuals to free ride on the protection afforded to them by others. We report the results of two vignette-based experiments conducted in the United States (n = 3,100) and Italy (n = 2,659) to examine the causal relationship between beliefs, social norms, and reported intentions to engage in mask promoting behavior. In both countries, survey respondents were quota sampled to be representative of the country's population on key demographics. We find that providing information about how masks protect others increases the likelihood that someone would wear a mask or encourage others to do so in the United States, but not in Italy. There is no effect of providing information about how masks protect the wearer in either country. Additionally, greater mask use increases intentions to wear a mask and encourage someone else to wear theirs properly in both the United States and Italy. Thus, community mask use may be self-reinforcing.

Citizens from 13 countries share similar preferences for COVID-19 vaccine allocation priorities.

How does the public want a COVID-19 vaccine to be allocated? We conducted a conjoint experiment asking 15,536 adults in 13 countries to evaluate 248,576 profiles of potential vaccine recipients who varied randomly on five attributes. Our sample includes diverse countries from all continents. The results suggest that in addition to giving priority to health workers and to those at high risk, the public favors giving priority to a broad range of key workers and to those with lower income. These preferences are similar across respondents of different education levels, incomes, and political ideologies, as well as across most surveyed countries. The public favored COVID-19 vaccines being allocated solely via government programs but were highly polarized in some developed countries on whether taking a vaccine should be mandatory. There is a consensus among the public on many aspects of COVID-19 vaccination, which needs to be taken into account when developing and communicating rollout strategies.

National interest may require distributing COVID-19 vaccines to other countries.

As immunization campaigns are accelerating, understanding how to distribute the scarce doses of vaccines is of paramount importance and a quantitative analysis of the trade-offs involved in domestic-only versus cooperative distribution is still missing. In this study we use a network Susceptible-Infected-Removed (SIR) model to show circumstances under which it is in a country's self-interest to ensure other countries can obtain COVID-19 vaccines rather than focusing only on vaccination of their own residents. In particular, we focus our analysis on the United States and estimate the internal burden of COVID-19 disease under different scenarios about vaccine cooperation. We show that in scenarios in which the US has reached the threshold for domestic herd immunity, the US may find it optimal to donate doses to other countries with lower vaccination coverage, as this would allow for a sharp reduction in the inflow of infected individuals from abroad.