Estimated public health impact of concurrent mask mandate and vaccinate-or-test requirement in Illinois, October to December 2021.

BACKGROUND: Facing a surge of COVID-19 cases in late August 2021, the U.S. state of Illinois re-enacted its COVID-19 mask mandate for the general public and issued a requirement for workers in certain professions to be vaccinated against COVID-19 or undergo weekly testing. The mask mandate required any individual, regardless of their vaccination status, to wear a well-fitting mask in an indoor setting. METHODS: We used Illinois Department of Public Health's COVID-19 confirmed case and vaccination data and investigated scenarios where masking and vaccination would have been reduced to mimic what would have happened had the mask mandate or vaccine requirement not been put in place. The study examined a range of potential reductions in masking and vaccination mimicking potential scenarios had the mask mandate or vaccine requirement not been enacted. We estimated COVID-19 cases and hospitalizations averted by changes in masking and vaccination during the period covering October 20 to December 20, 2021. RESULTS: We find that the announcement and implementation of a mask mandate are likely to correlate with a strong protective effect at reducing COVID-19 burden and the announcement of a vaccinate-or-test requirement among frontline professionals is likely to correlate with a more modest protective effect at reducing COVID-19 burden. In our most conservative scenario, we estimated that from the period of October 20 to December 20, 2021, the mask mandate likely prevented approximately 58,000 cases and 1,175 hospitalizations, while the vaccinate-or-test requirement may have prevented at most approximately 24,000 cases and 475 hospitalizations. CONCLUSION: Our results indicate that mask mandates and vaccine-or-test requirements are vital in mitigating the burden of COVID-19 during surges of the virus.

Public Health Response to Multistate Salmonella Typhimurium Outbreak Associated with Prepackaged Chicken Salad, United States, 2018.

Quantifying the effect of public health actions on population health is essential when justifying sustained public health investment. Using modeling, we conservatively estimated that rapid response to a multistate foodborne outbreak of Salmonella Typhimurium in the United States in 2018 potentially averted 94 reported cases and $633,181 in medical costs and productivity losses.

Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016.

Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.

Modeling the Transmission of COVID-19: Impact of Mitigation Strategies in Prekindergarten-Grade 12 Public Schools, United States, 2021.

BACKGROUND: Schools are an integral part of the community; however, congregate settings facilitate transmission of SARS-CoV-2, presenting a challenge to school administrators to provide a safe, in-school environment for students and staff. METHODS: We adapted the Centers for Disease Control and Prevention's COVIDTracer Advanced tool to model the transmission of SARS-CoV-2 in a school of 596 individuals. We estimate possible reductions in cases and hospitalizations among this population using a scenario-based analysis that accounts for (a) the risk of importation of infection from the community; (b) adherence to key Centers for Disease Control and Prevention-recommended mitigation strategies: mask wearing, cleaning and disinfection, hand hygiene, and social distancing; and (c) the effectiveness of contact tracing interventions at limiting onward transmission. RESULTS: Low impact and effectiveness of mitigation strategies (net effectiveness: 27%) result in approximately 40% of exposed staff and students becoming COVID-19 cases. When the net effectiveness of mitigation strategies was 69% or greater, in-school transmission was mostly prevented, yet importation of cases from the surrounding community could result in nearly 20% of the school's population becoming infected within 180 days. The combined effects of mitigation strategies and contact tracing were able to prevent most onward transmission. Hospitalizations were low among children and adults (<0.5% of the school population) across all scenarios examined. CONCLUSIONS: Based on our model, layering mitigation strategies and contact tracing can limit the number of cases that may occur from transmission in schools. Schools in communities with substantial levels of community spread will need to be more vigilant to ensure adherence of mitigation strategies to minimize transmission. Our results show that for school administrators, teachers, and parents to provide the safest environment, it is important to utilize multiple mitigation strategies and contract tracing that reduce SARS CoV-2 transmission by at least 69%. This will require training, reinforcement, and vigilance to ensure that the highest level of adherence is maintained over the entire school term.

Estimates of Cases and Hospitalizations Averted by COVID-19 Case Investigation and Contact Tracing in 14 Health Jurisdictions in the United States.

CONTEXT: The implementation of case investigation and contact tracing (CICT) for controlling COVID-19 (caused by SARS-CoV-2 virus) has proven challenging due to varying levels of public acceptance and initially constrained resources, especially enough trained staff. Evaluating the impacts of CICT will aid efforts to improve such programs. OBJECTIVES: Estimate the number of COVID-19 cases and hospitalizations averted by CICT and identify CICT processes that could improve overall effectiveness. DESIGN: We used data on the proportion of cases interviewed, contacts notified or monitored, and days from testing to case and contact notification from 14 jurisdictions to model the impact of CICT on cumulative case counts and hospitalizations over a 60-day period. Using the Centers for Disease Control and Prevention's COVIDTracer Advanced tool, we estimated a range of impacts by assuming either contacts would quarantine only if monitored or would do so upon notification of potential exposure. We also varied the observed program metrics to assess their relative influence. RESULTS: Performance by jurisdictions varied widely. Jurisdictions isolated between 12% and 86% of cases (including contacts that became cases) within 6 to 10 days after infection. We estimated that CICT-related reductions in transmission ranged from 0.4% to 32%. For every 100 remaining cases after other nonpharmaceutical interventions were implemented, CICT averted between 4 and 97 additional cases. Reducing time to case isolation by 1 day increased averted case estimates by up to 15 percentage points. Increasing the proportion of cases interviewed or contacts notified by 20 percentage points each resulted in at most 3 or 6 percentage point improvements in averted cases. CONCLUSIONS: We estimated that CICT reduced the number of COVID-19 cases and hospitalizations among all jurisdictions studied. Reducing time to isolation produced the greatest improvements in impact of CICT.

The PanVax Tool to Improve Pandemic Influenza Emergency Vaccination Program Readiness and Partnership.

Objectives. To show how the Centers for Disease Control and Prevention's Pandemic Vaccine Campaign Planning Tool (PanVax Tool) can help state and local public health emergency planners demonstrate and quantify how partnerships with community vaccine providers can improve their overall pandemic vaccination program readiness.Methods. The PanVax Tool helps planners compare different strategies to vaccinate their jurisdiction's population in a severe pandemic by allowing users to customize the underlying model inputs in real time, including their jurisdiction's size, community vaccine provider types, and how they allocate vaccine to these providers. In this report, we used a case study with hypothetical data to illustrate how jurisdictions can utilize the PanVax Tool for preparedness planning.Results. By using the tool, planners are able to understand the impact of engaging with different vaccine providers in a vaccination campaign.Conclusions. The PanVax Tool is a useful tool to help demonstrate the impact of community vaccine provider partnerships on pandemic vaccination readiness and identify areas for improved partnerships for pandemic response.

Building a Simple Model to Assess the Impact of Case Investigation and Contact Tracing for Sexually Transmitted Diseases: Lessons From COVID-19.

Introduction: During the COVID-19 pandemic, the U.S. Centers for Disease Control and Prevention developed a simple spreadsheet-based tool to help state and local public health officials assess the performance and impact of COVID-19 case investigation and contact tracing in their jurisdiction. The applicability and feasibility of building such a tool for sexually transmitted diseases were assessed. Methods: The key epidemiologic differences between sexually transmitted diseases and respiratory diseases (e.g., mixing patterns, incubation period, duration of infection, and the availability of treatment) were identified, and their implications for modeling case investigation and contact tracing impact with a simple spreadsheet tool were remarked on. Existing features of the COVID-19 tool that are applicable for evaluating the impact of case investigation and contact tracing for sexually transmitted diseases were also identified. Results: Our findings offer recommendations for the future development of a spreadsheet-based modeling tool for evaluating the impact of sexually transmitted disease case investigation and contact tracing efforts. Generally, we advocate for simplifying sexually transmitted disease-specific complexities and performing sensitivity analyses to assess uncertainty. The authors also acknowledge that more complex modeling approaches might be required but note that it is possible that a sexually transmitted disease case investigation and contact tracing tool could incorporate features from more complex models while maintaining a user-friendly interface. Conclusions: A sexually transmitted disease case investigation and contact tracing tool could benefit from the incorporation of key features of the COVID-19 model, namely its user-friendly interface. The inherent differences between sexually transmitted diseases and respiratory viruses should not be seen as a limitation to the development of such tool.

Estimated COVID-19 Cases and Hospitalizations Averted by Case Investigation and Contact Tracing in the US.

Importance: Evidence of the impact of COVID-19 case investigation and contact tracing (CICT) programs is lacking, but policy makers need this evidence to assess the value of such programs. Objective: To estimate COVID-19 cases and hospitalizations averted nationwide by US states' CICT programs. Design, Setting, and Participants: This decision analytical model study used combined data from US CICT programs (eg, proportion of cases interviewed, contacts notified or monitored, and days to case and contact notification) with incidence data to model outcomes of CICT over a 60-day period (November 25, 2020, to January 23, 2021). The study estimated a range of outcomes by varying assumed compliance with isolation and quarantine recommendations. Fifty-nine state and territorial health departments that received federal funding supporting COVID-19 pandemic response activities were eligible for inclusion. Data analysis was performed from July to September 2021. Exposure: Public health case investigation and contact tracing. Main Outcomes and Measures: The primary outcomes were numbers of cases and hospitalizations averted and the percentage of cases averted among cases not prevented by vaccination and other nonpharmaceutical interventions. Results: In total, 22 states and 1 territory reported all measures necessary for the analysis. These 23 jurisdictions covered 42.5% of the US population (approximately 140 million persons), spanned all 4 US Census regions, and reported data that reflected all 59 federally funded CICT programs. This study estimated that 1.11 million cases and 27 231 hospitalizations were averted by CICT programs under a scenario where 80% of interviewed cases and monitored contacts and 30% of notified contacts fully complied with isolation and quarantine guidance, eliminating their contributions to future transmission. As many as 1.36 million cases and 33 527 hospitalizations could have been prevented if all interviewed cases and monitored contacts had entered into and fully complied with isolation and quarantine guidelines upon being interviewed or notified. Across both scenarios and all jurisdictions, CICT averted an estimated median of 21.2% (range, 1.3%-65.8%) of the cases not prevented by vaccination and other nonpharmaceutical interventions. Conclusions and Relevance: These findings suggest that CICT programs likely had a substantial role in curtailing the pandemic in most jurisdictions during the 2020 to 2021 winter peak. Differences in outcomes across jurisdictions indicate an opportunity to further improve CICT effectiveness. These estimates demonstrate the potential benefits from sustaining and improving these programs.

Mathematical Model of the Role of Asymptomatic Infection in Outbreaks of Some Emerging Pathogens.

Preparation for outbreaks of emerging infectious diseases is often predicated on beliefs that we will be able to understand the epidemiological nature of an outbreak early into its inception. However, since many rare emerging diseases exhibit different epidemiological behaviors from outbreak to outbreak, early and accurate estimation of the epidemiological situation may not be straightforward in all cases. Previous studies have proposed considering the role of active asymptomatic infections co-emerging and co-circulating as part of the process of emergence of a novel pathogen. Thus far, consideration of the role of asymptomatic infections in emerging disease dynamics have usually avoided considering some important sets of influences. In this paper, we present and analyze a mathematical model to explore the hypothetical scenario that some (re)emerging diseases may actually be able to maintain stable, endemic circulation successfully in an entirely asymptomatic state. We argue that an understanding of this potential mechanism for diversity in observed epidemiological dynamics may be of considerable importance in understanding and preparing for outbreaks of novel and/or emerging diseases.

Preventive malaria treatment for contacts of patients with Ebola virus disease in the context of the west Africa 2014-15 Ebola virus disease response: an economic analysis.

BACKGROUND: After the detection of an Ebola virus disease outbreak in west Africa in 2014, one of the elements of the response was to contact trace and isolate patients in specialised Ebola treatment units (ETUs) at onset of fever. We aimed to assess the economic feasibility of administering preventive malaria treatment to all contacts of patients with Ebola virus disease, to prevent the onset of febrile malaria and subsequent admission to ETUs. METHODS: We used a decision tree model to analyse the costs of preventive malaria treatment (artemisinin-based combination treatment [ACT]) for all contacts of patients with Ebola virus disease (in terms of administration and averted ETU-stay costs) and benefits (in terms of averted ETU admissions) in west Africa, from a health-care provider perspective. The period of analyses was 1 year, which is roughly similar to the duration of the 2014-15 west Africa Ebola outbreak response. We calculated the intervention's cost per ETU admission averted (average cost-effectiveness ratio) by season (wet and dry), country (Liberia, Sierra Leone, and Guinea), and age of contact (<5 years, 5-14 years, and ≥15 years). We did sensitivity analyses to assess how results varied with malaria parasite prevalence (in children aged 2-10 years), daily cost of ETU stay (for Liberian malaria incidence levels), and compliance and effectiveness of preventive malaria treatment. FINDINGS: Administration of ACTs to contacts of patients with Ebola virus disease was cost saving for contacts of all ages in Liberia, Sierra Leone, and Guinea, in both seasons, from a health-care provider perspective. In the wet season, preventive malaria treatment was estimated to reduce the probability of a contact being admitted to an ETU by a maximum of 36% (in Guinea, for contacts aged <5 years), and a minimum of 10% (in Guinea and Sierra Leone, for those aged ≥15 years). Assuming 85% compliance and taking into account the African population pyramid, the intervention is expected to be cost saving in contacts of all age groups in areas with malaria parasite prevalence in children aged 2-10 years as low as 10%. In Liberia during the wet season, malaria preventive treatment was cost saving even when average daily bed-stay costs were as low as US$5 for children younger than 5 years, $9 for those aged 5-14 years, and $22 for those aged 15 years or older. INTERPRETATION: Administration of preventive malaria treatment to contacts of patients with Ebola virus disease should be considered by public health officials when addressing Ebola virus disease outbreaks in countries and seasons where malaria reaches high levels of transmission. FUNDING: Centers for Disease Control and Prevention.

Modeling in Real Time During the Ebola Response.

To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Evolutionary significance of the role of family units in a broader social system.

Indirect benefits to individual fitness in social species can be influenced by a variety of behavioral factors. Behaviors which support the fitness of kin provide indirect benefits in the form of evolutionary success of relatives. Further, individuals may obtain additional indirect benefits via participation in a well-organized social environment. Building on previous models of selfishly-motivated self-organizing societies, we explore the evolutionary trade-off between inclusion and maintenance of family groups and the ability of a population to sustain a well-organized social structure. Our results demonstrate that the interactions between Hamiltonian and organizationally-based indirect benefits to individual fitness interact to favor certain types of social affiliation traits. Conversely, we show how particular types of social affiliation dynamics may provide selective pressures to limit the size of behaviorally-defined familial groups. We present the first studies of the evolution of social complexity differentiating affiliation behavior between kin and non-kin.