Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

Postoperative Glycemic Response in High-Risk Type II Diabetics Receiving Below-Knee Amputation: Does Intraoperative Dexamethasone Make an Impact?

Despite known risks of hyperglycemia on postoperative complications, the influence of intraoperative dexamethasone on blood glucose has yet to be evaluated within the diabetic limb salvage population. This study aimed to assess the effect of intraoperative dexamethasone on postoperative blood glucose in diabetic patients undergoing atraumatic major lower extremity amputations. A single-center retrospective review of diabetic patients undergoing below-knee amputation between January 2017 and December 2022 was performed. Blood glucose levels for the 5 days before and after amputation were recorded and compared with the primary endpoints of postoperative hyperglycemia (>200 mg/dL) and glucose variability (>200 mg/dL). Cohorts were divided by patients who did and did not receive intraoperative administration of dexamethasone. Three hundred eighty-one were screened for eligibility with 180 patients included. Of these, 50 patients received dexamethasone intraoperatively (38.5%). Average pre- and postoperative blood glucose, rate of pre- and postoperative hyperglycemia, perioperative glucose variability, and postoperative dehiscence and infection were comparable between cohorts. On multivariate analysis, intraoperative administration of dexamethasone was not associated with postoperative hyperglycemia (p = .104) or perioperative blood glucose variability > 200 mg/dL (p = .334). Perioperative blood glucose variability > 200 mg/dL was associated with higher odds of surgical site infection (SSI) (odds ratio 5.12, p = .003). Administration of intravenous dexamethasone to diabetic patients undergoing below-knee amputation is not associated with postoperative hyperglycemia or complications. This study confirms previous findings that high glucose is a predictor of SSI. Concerted effort by a multidisciplinary team to attain tight glycemic control is critical to optimizing healing.

Primary Targeted Muscle Reinnervation in Above-Knee Amputations in Patients with Unsalvageable Limbs from Limb-Threatening Ischemia or Infection.

BACKGROUND: Amputees frequently suffer from chronic pain in both their residual limbs (RLP) and phantom limbs (PLP) following their amputation. Targeted muscle reinnervation (TMR) is a nerve transfer technique that has been demonstrated to improve pain secondarily and at time of amputation. The goal of this study is to report on the efficacy of primary TMR at time of above-knee level amputations in the setting of limb-threatening ischemia or infection. METHODS: This is a retrospective review of a single-surgeon experience with TMR in patients undergoing through- or above-knee level amputations from January 2018 to June 2021. Patient charts were reviewed for the comorbidities in the Charlson Comorbidity Index. Postoperative notes were assayed for presence and absence of RLP and PLP, overall pain severity, chronic narcotic use, ambulatory status, and complications. A control group of patients undergoing lower limb amputation who did not receive TMR from January 2014 to December 2017 was used for comparison. RESULTS: Forty-one patients with through- or above-knee level amputations and primary TMR were included in this study. The tibial and common peroneal nerves were transferred in all cases to motor branches to the gastrocnemius, semimembranosus, semitendinosus, and biceps femoris. Fifty-eight patients with through- or above-knee level amputations without TMR were included for comparison. The TMR group had significantly less overall pain (41.5 vs. 67.2%, p = 0.01), RLP (26.8 vs. 44.8%, p = 0.04), and PLP (19.5 vs. 43.1%, p = 0.02). There were no significant differences in complication rates. CONCLUSION: TMR can safely and effectively be performed at time of a through- and above-knee level amputation and improves pain outcomes.

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.

Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective: To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting: The entire United States. Participants: None. Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results: From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study.

Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments).

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

The risk of drug resistance during long-acting antimicrobial therapy.

The emergence of drug resistance during antimicrobial therapy is a major global health problem, especially for chronic infections like human immunodeficiency virus, hepatitis B and C, and tuberculosis. Sub-optimal adherence to long-term treatment is an important contributor to resistance risk. New long-acting drugs are being developed for weekly, monthly or less frequent dosing to improve adherence, but may lead to long-term exposure to intermediate drug levels. In this study, we analyse the effect of dosing frequency on the risk of resistance evolving during time-varying drug levels. We find that long-acting therapies can increase, decrease or have little effect on resistance, depending on the source (pre-existing or de novo) and degree of resistance, and rates of drug absorption and clearance. Long-acting therapies with rapid drug absorption, slow clearance and strong wild-type inhibition tend to reduce resistance caused by partially resistant strains in the early stages of treatment even if they do not improve adherence. However, if subpopulations of microbes persist and can reactivate during sub-optimal treatment, longer-acting therapies may substantially increase the resistance risk. Our results show that drug kinetics affect selection for resistance in a complicated manner, and that pathogen-specific models are needed to evaluate the benefits of new long-acting therapies.

A Systematic Review of Coronavirus Disease 2019 Vaccine Efficacy and Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Disease.

Billions of doses of coronavirus disease 2019 (COVID-19) vaccines have been administered globally, dramatically reducing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence and severity in some settings. Many studies suggest vaccines provide a high degree of protection against infection and disease, but precise estimates vary and studies differ in design, outcomes measured, dosing regime, location, and circulating virus strains. In this study, we conduct a systematic review of COVID-19 vaccines through February 2022. We included efficacy data from Phase 3 clinical trials for 15 vaccines undergoing World Health Organization Emergency Use Listing evaluation and real-world effectiveness for 8 vaccines with observational studies meeting inclusion criteria. Vaccine metrics collected include protection against asymptomatic infection, any infection, symptomatic COVID-19, and severe outcomes including hospitalization and death, for partial or complete vaccination, and against variants of concern Alpha, Beta, Gamma, Delta, and Omicron. We additionally review the epidemiological principles behind the design and interpretation of vaccine efficacy and effectiveness studies, including important sources of heterogeneity.

Spatiotemporal trends in bed bug metrics: New York City.

Bed bug outbreaks pose a major challenge in urban environments and cause significant strain on public resources. Few studies have systematically analyzed this insect epidemic or the potential effects of policies to combat bed bugs. Here we use three sources of administrative data to characterize the spatial-temporal trends of bed bug inquiries, complaints, and reports in New York City. Bed bug complaints have significantly decreased (p < 0.01) from 2014-2020, the absolute number of complaints per month dropping by half (875 average complaints per month to 440 average complaints per month); conversely, complaints for other insects including cockroaches and flies did not decrease over the same period. Despite the decrease of bed bug complaints, areas with reported high bed bug infestation tend to remain infested, highlighting the persistence of these pests. There are limitations to the datasets; still the evidence available suggests that interventions employed by New York City residents and lawmakers are stemming the bed bug epidemic and may serve as a model for other large cities.

The effect of eviction moratoria on the transmission of SARS-CoV-2.

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure.

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure.

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.

The effect of eviction moratoria on the transmission of SARS-CoV-2.

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess municipal eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.

Antigen-driven clonal selection shapes the persistence of HIV-1-infected CD4+ T cells in vivo.

Clonal expansion of infected CD4+ T cells is a major mechanism of HIV-1 persistence and a barrier to achieving a cure. Potential causes are homeostatic proliferation, effects of HIV-1 integration, and interaction with antigens. Here, we show that it is possible to link antigen responsiveness, the full proviral sequence, the integration site, and the T cell receptor ß-chain (TCRß) sequence to examine the role of recurrent antigenic exposure in maintaining the HIV-1 reservoir. We isolated CMV- and Gag-responding CD4+ T cells from 10 treated individuals. Proviral populations in CMV-responding cells were dominated by large clones, including clones harboring replication-competent proviruses. TCRß repertoires showed high clonality driven by converging adaptive responses. Although some proviruses were in genes linked to HIV-1 persistence (BACH2, STAT5B, MKL1), the proliferation of infected cells under antigenic stimulation occurred regardless of the site of integration. Paired TCRß and integration site analysis showed that infection could occur early or late in the course of a clone's response to antigen and could generate infected cell populations too large to be explained solely by homeostatic proliferation. Together, these findings implicate antigen-driven clonal selection as a major factor in HIV-1 persistence, a finding that will be a difficult challenge to eradication efforts.

Crowding and the shape of COVID-19 epidemics.

The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections.

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

Dissemination of COVID-19 in cities and riverine communities in Central Amazonia / Disseminação da COVID-19 em cidades e comunidades ribeirinhas da Amazônia Central

The population of small towns and riverine communities in Amazonia are extremely vulnerable to COVID-19 due to the absence of basic health care infrastructure in the region. The objective of this study was to (1) evaluate the effect of social distancing measures in the dissemination of COVID-19 in small towns, and (2) to estimate the impact of reducing contact between rural and urban populations on the contamination of riverine communities of Central Amazon. Our results indicate that social distancing can significantly reduce the speed of dissemination of COVID-19 in the population of small towns. However, we also observed that even in towns with intense actions to combat COVID-19, social distancing is below the ideal 70% isolation mark. Regarding the contamination of riverine communities, we observed that the reduction in the number of visits and duration of stay, as well as the interruption of visits during the five weeks with larger probability of contamination has major impact on delaying the arrival of COVID-19 in these vulnerable populations. We recommend that, given the low effectiveness of social distancing measures and the rapid contamination of urban populations, each municipality should evaluate implementing more restrictive measures such as a full or partial lockdown. Our results also suggest that three measures can be effective in delaying the arrival of COVID-19 in riverine communities of Amazonia: (1) the reduction in the number of visits that each community makes to a town, (2) the reduction in duration of each visit, and (3) avoiding visits during the five weeks with the largest number of infected people in towns. It is imperative that implementation of any of the restrictive measures suggested in this publication be accompanied by a vast communication campaign as well as social assistance actions for the rural population and poor families of small towns to guarantee their basic needs for survival.

As populações da Amazônia são extremamente vulneráveis à COVID-19 devido ao intenso fluxo entre as localidades rurais e as sedes municipais e à falta de infraestrutura de saúde básica adequada na região. O objetivo deste estudo foi (1) avaliar o efeito da medida de distanciamento social sobre a disseminação da COVID-19 em sedes municipais e (2) estimar o impacto da redução do contato entre a população rural e urbana sobre a contaminação de localidades rurais da Amazônia Central, região do Médio Solimões. Nossos resultados indicam que o distanciamento social pode reduzir significativamente a velocidade de contaminação da população em sedes municipais. No entanto, mesmo em sedes municipais com atividade intensa no combate à COVID-19, o distanciamento social está muito abaixo do ideal de 70%. Observamos também que a redução do número de visitas e do tempo de permanência durante cada visita, além da interrupção do fluxo durante as cinco semanas com maior probabilidade infecção têm impacto marcante sobre a velocidade de chegada da COVID-19 nas localidades rurais. Recomendamos que, dada a baixa efetividade de medidas de distanciamento social e o rápido contágio nas sedes municipais, cada município avalie a implementação de lockdown. Nossos resultados sugerem também que três medidas podem ser eficientes em atrasar a chegada da COVID-19 às localidades rurais: (1) a redução no número de visitas que cada uma faz a um centro urbano, (2) a redução do tempo de permanência durante cada visita e (3) evitar qualquer ida às sedes municipais durante as cinco semanas com maior número de pessoas infectadas. Destacamos que todas as medidas restritivas sugeridas precisam ser acompanhadas por uma ampla campanha de conscientização e ações de assistência à população rural e às famílias mais carentes das sedes municipais para garantir acesso a itens básicos para sua sobrevivência.