Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Phylogeography and transmission of M. tuberculosis in Moldova: A prospective genomic analysis.

BACKGROUND: The incidence of multidrug-resistant tuberculosis (MDR-TB) remains critically high in countries of the former Soviet Union, where >20% of new cases and >50% of previously treated cases have resistance to rifampin and isoniazid. Transmission of resistant strains, as opposed to resistance selected through inadequate treatment of drug-susceptible tuberculosis (TB), is the main driver of incident MDR-TB in these countries. METHODS AND FINDINGS: We conducted a prospective, genomic analysis of all culture-positive TB cases diagnosed in 2018 and 2019 in the Republic of Moldova. We used phylogenetic methods to identify putative transmission clusters; spatial and demographic data were analyzed to further describe local transmission of Mycobacterium tuberculosis. Of 2,236 participants, 779 (36%) had MDR-TB, of whom 386 (50%) had never been treated previously for TB. Moreover, 92% of multidrug-resistant M. tuberculosis strains belonged to putative transmission clusters. Phylogenetic reconstruction identified 3 large clades that were comprised nearly uniformly of MDR-TB: 2 of these clades were of Beijing lineage, and 1 of Ural lineage, and each had additional distinct clade-specific second-line drug resistance mutations and geographic distributions. Spatial and temporal proximity between pairs of cases within a cluster was associated with greater genomic similarity. Our study lasted for only 2 years, a relatively short duration compared with the natural history of TB, and, thus, the ability to infer the full extent of transmission is limited. CONCLUSIONS: The MDR-TB epidemic in Moldova is associated with the local transmission of multiple M. tuberculosis strains, including distinct clades of highly drug-resistant M. tuberculosis with varying geographic distributions and drug resistance profiles. This study demonstrates the role of comprehensive genomic surveillance for understanding the transmission of M. tuberculosis and highlights the urgency of interventions to interrupt transmission of highly drug-resistant M. tuberculosis.

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns.

There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.

The Impact of Changes in Diagnostic Testing Practices on Estimates of COVID-19 Transmission in the United States.

Estimates of the reproductive number for novel pathogens, such as severe acute respiratory syndrome coronavirus 2, are essential for understanding the potential trajectory of epidemics and the levels of intervention that are needed to bring the epidemics under control. However, most methods for estimating the basic reproductive number (R0) and time-varying effective reproductive number (Rt) assume that the fraction of cases detected and reported is constant through time. We explored the impact of secular changes in diagnostic testing and reporting on estimates of R0 and Rt using simulated data. We then compared these patterns to data on reported cases of coronavirus disease 2019 and testing practices from different states in the United States from March 4, 2020, to August 30, 2020. We found that changes in testing practices and delays in reporting can result in biased estimates of R0 and Rt. Examination of changes in the daily numbers of tests conducted and the percentages of patients who tested positive might be helpful for identifying the potential direction of bias. Changes in diagnostic testing and reporting processes should be monitored and taken into consideration when interpreting estimates of the reproductive number of coronavirus disease.

Immunologic and Epidemiologic Drivers of Norovirus Transmission in Daycare and School Outbreaks.

BACKGROUND: Norovirus outbreaks are notoriously explosive, with dramatic symptomology and rapid disease spread. Children are particularly vulnerable to infection and drive norovirus transmission due to their high contact rates with each other and the environment. Despite the explosive nature of norovirus outbreaks, attack rates in schools and daycares remain low with the majority of students not reporting symptoms. METHODS: We explore immunologic and epidemiologic mechanisms that may underlie epidemic norovirus transmission dynamics using a disease transmission model. Towards this end, we compared different model scenarios, including innate resistance and acquired immunity (collectively denoted 'immunity'), stochastic extinction, and an individual exclusion intervention. We calibrated our model to daycare and school outbreaks from national surveillance data. RESULTS: Including immunity in the model led to attack rates that were consistent with the data. However, immunity alone resulted in the majority of outbreak durations being relatively short. The addition of individual exclusion (to the immunity model) extended outbreak durations by reducing the amount of time that symptomatic people contribute to transmission. Including both immunity and individual exclusion mechanisms resulted in simulations where both attack rates and outbreak durations were consistent with surveillance data. CONCLUSIONS: The epidemiology of norovirus outbreaks in daycare and school settings cannot be well described by a simple transmission model in which all individuals start as fully susceptible. More studies on how best to design interventions which leverage population immunity and encourage more rigorous individual exclusion may improve venue-level control measures. See video abstract at http://links.lww.com/EDE/B795.

Understanding the Importance of Contact Heterogeneity and Variable Infectiousness in the Dynamics of a Large Norovirus Outbreak.

BACKGROUND: Large norovirus (NoV) outbreaks are explosive in nature and vary widely in final size and duration, suggesting that superspreading combined with heterogeneous contact may explain these dynamics. Modeling tools that can capture heterogeneity in infectiousness and contact are important for NoV outbreak prevention and control, yet they remain limited. METHODS: Data from a large NoV outbreak at a Dutch scout jamboree, which resulted in illness among 326 (of 4500 total) individuals from 7 separate camps, were used to examine the contributions of individual variation in infectiousness and clustered contact patterns to the transmission dynamics. A Bayesian hierarchical model of heterogeneous, clustered outbreak transmission was applied to represent (1) between-individual heterogeneity in infectiousness and (2) heterogeneous patterns of contact. RESULTS: We found wide heterogeneity in infectiousness across individuals, suggestive of superspreading. Nearly 50% of individual infectiousness was concentrated in the individual's subcamp of residence, with the remainder distributed over other subcamps. This suggests a source-and-sink dynamic in which subcamps with greater average infectiousness fed cases to those with a lower transmission rate. Although the per capita transmission rate within camps was significantly greater than that between camps, the large pool of susceptible individuals across camps enabled similar numbers of secondary cases generated between versus within camps. CONCLUSIONS: The consideration of clustered transmission and heterogeneous infectiousness is important for understanding NoV transmission dynamics. Models including these mechanisms may be useful for providing early warning and guiding outbreak response.

Comparing alternative cholera vaccination strategies in Maela refugee camp: using a transmission model in public health practice.

BACKGROUND: Cholera is a major public health concern in displaced-person camps, which often contend with overcrowding and scarcity of resources. Maela, the largest and longest-standing refugee camp in Thailand, located along the Thai-Burmese border, experienced four cholera outbreaks between 2005 and 2010. In 2013, a cholera vaccine campaign was implemented in the camp. To assist in the evaluation of the campaign and planning for subsequent campaigns, we developed a mathematical model of cholera in Maela. METHODS: We formulated a Susceptible-Infectious-Water-Recovered-based transmission model and estimated parameters using incidence data from 2010. We next evaluated the reduction in cases conferred by several immunization strategies, varying timing, effectiveness, and resources (i.e., vaccine availability). After the vaccine campaign, we generated case forecasts for the next year, to inform on-the-ground decision-making regarding whether a booster campaign was needed. RESULTS: We found that preexposure vaccination can substantially reduce the risk of cholera even when <50% of the population is given the full two-dose series. Additionally, the preferred number of doses per person should be considered in the context of one vs. two dose effectiveness and vaccine availability. For reactive vaccination, a trade-off between timing and effectiveness was revealed, indicating that it may be beneficial to give one dose to more people rather than two doses to fewer people, given that a two-dose schedule would incur a delay in administration of the second dose. Forecasting using realistic coverage levels predicted that there was no need for a booster campaign in 2014 (consistent with our predictions, there was not a cholera epidemic in 2014). CONCLUSIONS: Our analyses suggest that vaccination in conjunction with ongoing water sanitation and hygiene efforts provides an effective strategy for controlling cholera outbreaks in refugee camps. Effective preexposure vaccination depends on timing and effectiveness. If a camp is facing an outbreak, delayed distribution of vaccines can substantially alter the effectiveness of reactive vaccination, suggesting that quick distribution of vaccines may be more important than ensuring every individual receives both vaccine doses. Overall, this analysis illustrates how mathematical models can be applied in public health practice, to assist in evaluating alternative intervention strategies and inform decision-making.

Detection of Mycobacterium tuberculosis pncA Mutations by the Nipro Genoscholar PZA-TB II Assay Compared to Conventional Sequencing.

Pyrazinamide (PZA) is a standard component of first-line treatment regimens for Mycobacterium tuberculosis and is included in treatment regimens for drug-resistant M. tuberculosis whenever possible. Therefore, it is imperative that susceptibility to PZA be assessed reliably prior to the initiation of therapy. Currently available growth-based PZA susceptibility tests are time-consuming, and results can be inconsistent. Molecular tests have been developed for most first-line antituberculosis drugs; however, a commercial molecular test is not yet available for rapid detection of PZA resistance. Recently, a line probe assay, the Nipro Genoscholar PZA-TB II assay, was developed for the detection of mutations within the pncA gene, including the promoter region, that are likely to lead to PZA resistance. The sensitivity and specificity of this assay were evaluated by two independent laboratories, using a combined total of 249 strains with mutations in pncA or its promoter and 21 strains with wild-type pncA Overall, the assay showed good sensitivity (93.2% [95% confidence interval, 89.3 to 95.8%]) and moderate specificity (91.2% [95% confidence interval, 77.0 to 97.0%]) for the identification of M. tuberculosis strains predicted to be resistant to PZA on the basis of the presence of mutations (excluding known PZA-susceptible mutations) in the pncA coding region or promoter. The assay shows promise for the molecular prediction of PZA resistance.

Molecular Detection of Mycobacterium tuberculosis from Stools in Young Children by Use of a Novel Centrifugation-Free Processing Method.

The microbiological diagnosis of tuberculosis (TB) in children is challenging, as it relies on the collection of relatively invasive specimens by trained health care workers, which is not feasible in many settings. Mycobacterium tuberculosis is detectable from the stools of children using molecular methods, but processing stool specimens is resource intensive. We evaluated a novel, simple, centrifugation-free processing method for stool specimens for use on the Xpert MTB/RIF assay (Xpert), using two different stool masses: 0.6 g and a swab sample. Two hundred eighty children (median age, 15.5 months; 35 [12.5%] HIV infected) with suspected intrathoracic TB were enrolled from two sites in South Africa. Compared to a single Xpert test on respiratory specimens, the sensitivity of Xpert on stools using the 0.6-g and swab samples was 44.4% (95% confidence interval [CI], 13.7 to 78.8%) for both methods, with a specificity of >99%. The combined sensitivities of two stool tests versus the first respiratory Xpert were 70.0% (95% CI, 34.8 to 93.3) and 50.0% (95% CI, 18.7 to 81.3) for the 0.6-g and swab sample, respectively. Retesting stool specimens with nondeterminate Xpert results improved nondeterminate rates from 9.3% to 3.9% and from 8.6% to 4.3% for 0.6-g and swab samples, respectively. Overall, stool Xpert detected 14/94 (14.9%) children who initiated antituberculosis treatment, while respiratory specimens detected 23/94 (24.5%). This stool processing method is well suited for settings with low capacity for respiratory specimen collection. However, the overall sensitivity to detect confirmed and clinical TB was lower than that of respiratory specimens. More sensitive rapid molecular assays are needed to improve the utility of stools for the diagnosis of intrathoracic TB in children from resource-limited settings.

The inverted cup device for blood transfer on malaria RDTs: ease of use, acceptability and safety in routine use by health workers in Nigeria.

BACKGROUND: Malaria rapid diagnostic tests (RDTs) are becoming widely adopted for case management at community level. However, reports and anecdotal observations indicate that the blood transfer step poses a significant challenge to many users. This study sought to evaluate the inverted cup device in the hands of health workers in everyday clinical practice, in comparison with the plastic pipette, and to determine the volume accuracy of the device made of a lower-cost plastic. METHODS: The volume accuracy of inverted cup devices made of two plastics, PMMA and SBC, was compared by transferring blood 150 times onto filter paper and comparing the blood spot areas with those produced by 20 reference transfers with a calibrated micropipette. The ease of use, safety and acceptability of the inverted cup device and the pipette were evaluated by 50 health workers in Nigeria. Observations were recorded on pre-designed questionnaires, by the health workers themselves and by trained observers. Focus group discussions were also conducted. RESULTS: The volume accuracy assessment showed that the device made from the low-cost material (SBC) delivered a more accurate volume (mean 5.4 µL, SD 0.48 µL, range 4.5-7.0 µL) than the PMMA device (mean 5.9 µL, SD 0.48 µL, range 4.9-7.2 µL). The observational evaluation demonstrated that the inverted cup device performed better than the pipette in all aspects, e.g. higher proportions of health workers achieved successful blood collection (96%, vs. 66%), transfer of the required blood volume (90%, vs. 58%), and blood deposit without any loss (95%, vs. 50%). Majority of health workers also considered it' very easy' to use (81%),'very appropriate' for everyday use (78%), and 50% of them reported that it was their preferred BTD. CONCLUSIONS: The good volume accuracy and high acceptability of the inverted cup device shown in this study, along with observed ease of use and safety in hands of health workers, further strengthens prior findings which demonstrated its higher accuracy as compared with other BTDs in a laboratory setting. Altogether, these studies suggest that the inverted cup device should replace other types of devices for use in day-to-day malaria diagnosis with RDTs.

Diagnostic Performance of Tuberculosis-Specific IgG Antibody Profiles in Patients with Presumptive Tuberculosis from Two Continents.

BACKGROUND: Development of rapid diagnostic tests for tuberculosis is a global priority. A whole proteome screen identified Mycobacterium tuberculosis antigens associated with serological responses in tuberculosis patients. We used World Health Organization (WHO) target product profile (TPP) criteria for a detection test and triage test to evaluate these antigens. METHODS: Consecutive patients presenting to microscopy centers and district hospitals in Peru and to outpatient clinics at a tuberculosis reference center in Vietnam were recruited. We tested blood samples from 755 HIV-uninfected adults with presumptive pulmonary tuberculosis to measure IgG antibody responses to 57 M. tuberculosis antigens using a field-based multiplexed serological assay and a 132-antigen bead-based reference assay. We evaluated single antigen performance and models of all possible 3-antigen combinations and multiantigen combinations. RESULTS: Three-antigen and multiantigen models performed similarly and were superior to single antigens. With specificity set at 90% for a detection test, the best sensitivity of a 3-antigen model was 35% (95% confidence interval [CI], 31-40). With sensitivity set at 85% for a triage test, the specificity of the best 3-antigen model was 34% (95% CI, 29-40). The reference assay also did not meet study targets. Antigen performance differed significantly between the study sites for 7/22 of the best-performing antigens. CONCLUSIONS: Although M. tuberculosis antigens were recognized by the IgG response during tuberculosis, no single antigen or multiantigen set performance approached WHO TPP criteria for clinical utility among HIV-uninfected adults with presumed tuberculosis in high-volume, urban settings in tuberculosis-endemic countries.

Antimicrobial resistance in Africa: a systematic review.

BACKGROUND: Antimicrobial resistance (AMR) is widely acknowledged as a global problem, yet in many parts of the world its magnitude is still not well understood. This review, using a public health focused approach, aimed to understand and describe the current status of AMR in Africa in relation to common causes of infections and drugs recommended in WHO treatment guidelines. METHODS: PubMed, EMBASE and other relevant databases were searched for recent articles (2013-2016) in accordance with the PRISMA guidelines. Article retrieval and screening were done using a structured search string and strict inclusion/exclusion criteria. Median and interquartile ranges of percent resistance were calculated for each antibiotic-bacterium combination. RESULTS: AMR data was not available for 42.6% of the countries in the African continent. A total of 144 articles were included in the final analysis. 13 Gram negative and 5 Gram positive bacteria were tested against 37 different antibiotics. Penicillin resistance in Streptococcus pneumoniae was reported in 14/144studies (median resistance (MR): 26.7%). Further 18/53 (34.0%) of Haemophilus influenza isolates were resistant to amoxicillin. MR of Escherichia coli to amoxicillin, trimethoprim and gentamicin was 88.1%, 80.7% and 29.8% respectively. Ciprofloxacin resistance in Salmonella Typhi was rare. No documented ceftriaxone resistance in Neisseria gonorrhoeae was reported, while the MR for quinolone was 37.5%. Carbapenem resistance was common in Acinetobacter spp. and Pseudomonas aeruginosa but uncommon in Enterobacteriaceae. CONCLUSION: Our review highlights three important findings. First, recent AMR data is not available for more than 40% of the countries. Second, the level of resistance to commonly prescribed antibiotics was significant. Third, the quality of microbiological data is of serious concern. Our findings underline that to conserve our current arsenal of antibiotics it is imperative to address the gaps in AMR diagnostic standardization and reporting and use available information to optimize treatment guidelines.

Multicenter Noninferiority Evaluation of Hain GenoType MTBDRplus Version 2 and Nipro NTM+MDRTB Line Probe Assays for Detection of Rifampin and Isoniazid Resistance.

Less than 30% of multidrug-resistant tuberculosis (MDR-TB) patients are currently diagnosed, due to laboratory constraints. Molecular diagnostics enable rapid and simplified diagnosis. Newer-version line probe assays have not been evaluated against the WHO-endorsed Hain GenoType MTBDRplus (referred to as Hain version 1 [V1]) for the rapid detection of rifampin (RIF) and isoniazid (INH) resistance. A two-phase noninferiority study was conducted in two supranational reference laboratories to allow head-to-head comparisons of two new tests, Hain Genotype MTBDRplus version 2 (referred to as Hain version 2 [V2]) and Nipro NTM+MDRTB detection kit 2 (referred to as Nipro), to Hain V1. In phase 1, the results for 379 test strains were compared to a composite reference standard that used phenotypic drug susceptibility testing (DST) and targeted sequencing. In phase 2, the results for 644 sputum samples were compared to a phenotypic DST reference standard alone. Using a challenging set of strains in phase 1, the values for sensitivity and specificity for Hain V1, Hain V2, and Nipro, respectively, were 90.3%/98.5%, 90.3%/98.5%, and 92.0%/98.5% for RIF resistance detection and 89.1%/99.4%, 89.1%/99.4%, and 89.6%/100.0% for INH resistance detection. Testing of sputa in phase 2 yielded values for sensitivity and specificity of 97.1%/97.1%, 98.2%/97.8%, and 96.5%/97.5% for RIF and 94.4%/96.4%, 95.4%/98.8%, and 94.9%/97.6% for INH. Overall, the rates of indeterminate results were low, but there was a higher rate of indeterminate results with Nipro than with Hain V1 and V2 in samples with low smear grades. Noninferiority of Hain V2 and Nipro to Hain V1 was demonstrated for RIF and INH resistance detection in isolates and sputum specimens. These results serve as evidence for WHO policy recommendations on the use of line probe assays, including the Hain V2 and Nipro assays, for MDR-TB detection.

The role of public policies in reducing smoking prevalence: results from the Michigan SimSmoke tobacco policy simulation model.

INTRODUCTION: Michigan has implemented several of the tobacco control policies recommended by the World Health Organization MPOWER goals. We consider the effect of those policies and additional policies consistent with MPOWER goals on smoking prevalence and smoking-attributable deaths (SADs). METHODS: The SimSmoke tobacco control policy simulation model is used to examine the effect of past policies and a set of additional policies to meet the MPOWER goals. The model is adapted to Michigan using state population, smoking, and policy data starting in 1993. SADs are estimated using standard attribution methods. Upon validating the model, SimSmoke is used to distinguish the effect of policies implemented since 1993 against a counterfactual with policies kept at their 1993 levels. The model is then used to project the effect of implementing stronger policies beginning in 2014. RESULTS: SimSmoke predicts smoking prevalence accurately between 1993 and 2010. Since 1993, a relative reduction in smoking rates of 22 % by 2013 and of 30 % by 2054 can be attributed to tobacco control policies. Of the 22 % reduction, 44 % is due to taxes, 28 % to smoke-free air laws, 26 % to cessation treatment policies, and 2 % to youth access. Moreover, 234,000 SADs are projected to be averted by 2054. With additional policies consistent with MPOWER goals, the model projects that, by 2054, smoking prevalence can be further reduced by 17 % with 80,000 deaths averted relative to the absence of those policies. CONCLUSIONS: Michigan SimSmoke shows that tobacco control policies, including cigarette taxes, smoke-free air laws, and cessation treatment policies, have substantially reduced smoking and SADs. Higher taxes, strong mass media campaigns, and cessation treatment policies would further reduce smoking prevalence and SADs.

Coverage, use and maintenance of bed nets and related influence factors in Kachin Special Region II, northeastern Myanmar.

BACKGROUND: Myanmar is one of the 31 highest burden malaria countries worldwide. Scaling up the appropriate use of insecticide-treated nets (ITNs) is a national policy for malaria prevention and control. However, the data on use, influencing factors and maintenance of bed nets is still lack among the population in Kachin Special Region II (KR2), Northeastern Myanmar. METHODS: The study combined a quantitative household questionnaire survey and qualitative direct observation of households. A Chi-squared test was used to compare the percentages of ownership, coverage, and rates of use of bed nets. Additionally, multivariate logistic regression analysis (MVLRA) was used to analyse factors that influence the use of bed nets. Finally, covariance compared the mean calibrated hole indexes (MCHI) across potential influence variables. RESULTS: The bed net to person ratio was 1:1.96 (i.e., more than one net for every two people). The long-lasting insecticidal net (LLIN) to person ratio was 1: 2.52. Also, the percentage of households that owned at least one bed net was 99.7% (666/688). Some 3262 (97.3%) residents slept under bed nets the prior night, 2551 (76.1%) of which slept under ITNs/LLINs the prior night (SUITNPN). The poorest families, those with thatched roofing, those who use agriculture as their main source of family income, household heads who knew that mosquitoes transmit malaria and those who used bed nets to prevent malaria, were significantly more likely to be in the SUITNPN group. However, residents in lowlands, and foothills were significantly less likely to be SUITNPNs. Finally, head of household attitude towards fixing bed nets influenced MCHI (F=8.09, P=0.0046). CONCLUSIONS: The coverage and usage rates of bed nets were high, especially among children, and pregnant women. Family wealth index, geographical zones, household roofing, source of family income, household head's knowledge of malaria transmission and of using bed nets as tools for malaria prevention are all independent factors which influence use of ITNs/LLINs in KR2. Maintaining high coverage, and use rate of bed nets should be a priority for the war-torn population of KR2 to ensure equity and human rights.

Spatially-targeted tuberculosis screening has limited impact beyond household contact tracing in Lima, Peru: A model-based analysis.

Mathematical models have suggested that spatially-targeted screening interventions for tuberculosis may efficiently accelerate disease control, but empirical data supporting these findings are limited. Previous models demonstrating substantial impacts of these interventions have typically simulated large-scale screening efforts and have not attempted to capture the spatial distribution of tuberculosis in households and communities at a high resolution. Here, we calibrate an individual-based model to the locations of case notifications in one district of Lima, Peru. We estimate the incremental efficiency and impact of a spatially-targeted interventions used in combination with household contact tracing (HHCT). Our analysis reveals that HHCT is relatively efficient with a median of 40 (Interquartile Range: 31.7 to 49.9) household contacts required to be screened to detect a single case of active tuberculosis. However, HHCT has limited population impact, producing a median incidence reduction of only 3.7% (Interquartile Range: 5.8% to 1.9%) over 5 years. In comparison, spatially targeted screening (which we modeled as active case finding within high tuberculosis prevalence areas 100 m2 grid cell) is far less efficient, requiring evaluation of ≈12 times the number of individuals as HHCT to find a single individual with active tuberculosis. Furthermore, the addition of the spatially targeted screening effort produced only modest additional reductions in tuberculosis incidence over the 5 year period (≈1.3%) in tuberculosis incidence. In summary, we found that HHCT is an efficient approach for tuberculosis case finding, but has limited population impact. Other screening approaches which target areas of high tuberculosis prevalence are less efficient, and may have limited impact unless very large numbers of individuals can be screened.

Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic.

Policy makers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We describe a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions.

Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: results of a Bayesian evidence synthesis model.

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 400,718 COVID-19 deaths by the end of 2020, and that 27% of the US population had been infected. The results also demonstrate wide county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Cost-effectiveness of pediatric norovirus vaccination in daycare settings.

OBJECTIVE: Noroviruses are the leading cause of acute gastroenteritis in the United States and outbreaks frequently occur in daycare settings. Results of norovirus vaccine trials have been promising, however there are open questions as to whether vaccination of daycare children would be cost-effective. We investigated the incremental cost-effectiveness of a hypothetical norovirus vaccination for children in daycare settings compared to no vaccination. METHODS: We conducted a model-based cost-effectiveness analysis using a disease transmission model of children attending daycare. Vaccination with a 90% coverage rate in addition to the observed standard of care (exclusion of symptomatic children from daycare) was compared to the observed standard of care. The main outcomes measures were infections and deaths averted, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER). Cost-effectiveness was analyzed from a societal perspective, including medical costs to children as well as productivity losses of parents, over a two-year time horizon. Data sources included outbreak surveillance data and published literature. RESULTS: A 50% efficacious norovirus vaccine averts 571.83 norovirus cases and 0.003 norovirus-related deaths per 10,000 children compared to the observed standard of care. A $200 norovirus vaccine that is 50% efficacious has a net cost increase of $178.10 per child and 0.025 more QALYs, resulting in an ICER of $7,028/QALY. Based on the probabilistic sensitivity analysis, we estimated that a $200 vaccination with 50% efficacy was 94.0% likely to be cost-effective at a willingness-to-pay of $100,000/QALY threshold and 95.3% likely at a $150,000/QALY threshold. CONCLUSION: Due to the large disease burden associated with norovirus, it is likely that vaccinating children in daycares could be cost-effective, even with modest vaccine efficacy and a high per-child cost of vaccination. Norovirus vaccination of children in daycare has a cost-effectiveness ratio similar to other commonly recommended childhood vaccines.

Using compartmental models to simulate directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data.

Accurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG-derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g. reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.