COVID-19 Vaccination and Incidence of Pediatric SARS-CoV-2 Infection and Hospitalization.

Importance: A SARS-CoV-2 vaccine was approved for adolescents aged 12 to 15 years on May 10, 2021, with approval for younger age groups following thereafter. The population level impact of the pediatric COVID-19 vaccination program has not yet been established. Objective: To identify whether California's pediatric COVID-19 immunization program was associated with changes in pediatric COVID-19 incidence and hospitalizations. Design, Setting, and Participants: A case series on COVID-19 vaccination including children aged 6 months to 15 years was conducted in California. Data were obtained on COVID-19 cases in California between April 1, 2020, and February 27, 2023. Exposure: Postvaccination evaluation periods spanned 141 days (June 10 to October 29, 2021) for adolescents aged 12 to 15 years, 199 days (November 29, 2021, to June 17, 2022) for children aged 5 to 11 years, and 225 days (July 17, 2022, to February 27, 2023) for those aged 6 to 59 months. During these periods, statewide vaccine coverage reached 53.5% among adolescents aged 12 to 15 years, 34.8% among children aged 5 to 11 years, and 7.9% among those aged 6 to 59 months. Main Outcomes and Measures: Age-stepped implementation of COVID-19 vaccination was used to compare observed county-level incidence and hospitalization rates during periods when each age group became vaccine eligible to counterfactual rates predicted from observations among other age groups. COVID-19 case and hospitalization data were obtained from the California reportable disease surveillance system. Results: Between April 1, 2020, and February 27, 2023, a total of 3 913 063 pediatric COVID-19 cases and 12 740 hospitalizations were reported in California. Reductions of 146 210 cases (95% prediction interval [PI], 136 056-158 948) were estimated among adolescents aged 12 to 15 years, corresponding to a 37.1% (35.5%-39.1%) reduction from counterfactual predictions. Reductions of 230 134 (200 170-265 149) cases were estimated among children aged 5 to 11 years, corresponding to a 23.7% (20.6%-27.3%) reduction from counterfactual predictions. No evidence of reductions in COVID-19 cases statewide were found among children aged 6 to 59 months (estimated averted cases, -259; 95% PI, -1938 to 1019), although low transmission during the evaluation period may have limited the ability to do so. An estimated 168 hospitalizations (95% PI, 42-324) were averted among children aged 6 to 59 months, corresponding to a 24.4% (95% PI, 6.1%-47.1%) reduction. In meta-analyses, county-level vaccination coverage was associated with averted cases for all age groups. Despite low vaccination coverage, pediatric COVID-19 immunization in California averted 376 085 (95% PI, 348 355-417 328) reported cases and 273 (95% PI, 77-605) hospitalizations among children aged 6 months to 15 years over approximately 4 to 7 months following vaccination availability. Conclusions and Relevance: The findings of this case series analysis of 3 913 063 cases suggest reduced pediatric SARS-CoV-2 transmission following immunization. These results support the use of COVID-19 vaccines to reduce COVID-19 incidence and hospitalization in pediatric populations.

Interim Influenza Vaccine Effectiveness Against Laboratory-Confirmed Influenza - California, October 2023-January 2024.

Surveillance data can provide rapid, within-season influenza vaccine effectiveness (VE) estimates to guide public health recommendations. Mandatory reporting of influenza vaccine administration to California's immunization information registry began January 1, 2023, and mandatory reporting of all influenza laboratory test results, including negative results, was instituted in California on June 15, 2023. These data, collected by the California Department of Public Health during October 1, 2023-January 31, 2024, were used to calculate interim influenza VE against laboratory-confirmed influenza by comparing the odds of vaccination among case-patients (persons who received a positive influenza laboratory test result) and control patients (those who received a negative influenza laboratory test result). VE was calculated as 1 - adjusted odds ratio using mixed-effects logistic regression, with age, race, and ethnicity as fixed effects and specimen collection week and county as random effects. Overall, during October 1, 2023-January 31, 2024, estimated VE was 45% among persons aged ≥6 months, 56% among children and adolescents aged 6 months-17 years, 48% among adults aged 18-49 years, 36% among those aged 50-64 years, and 30% among those aged ≥65 years. Consistent with some previous influenza seasons, influenza vaccination provided moderate protection against laboratory-confirmed influenza among infants, children, adolescents, and adults. All persons aged ≥6 months without a contraindication to vaccination should receive annual influenza vaccination to reduce influenza illness, severe influenza, and strain on health care resources. Influenza vaccination remains the best way to prevent influenza.

Predicting the Public Health Impact of Bivalent Vaccines and Nirmatrelvir-Ritonavir Against Coronavirus Disease 2019.

Background: Uptake of coronavirus disease 2019 (COVID-19) bivalent vaccines and the oral medication nirmatrelvir-ritonavir (Paxlovid) has remained low across the United States. Assessing the public health impact of increasing uptake of these interventions in key risk groups can guide further public health resources and policy and determine what proportion of severe COVID-19 is avertable with these interventions. Methods: This modeling study used person-level data from the California Department of Public Health on COVID-19 cases, hospitalizations, deaths, and vaccine administration from 23 July 2022 to 23 January 2023. We used a quasi-Poisson regression model calibrated to recent historical data to predict future COVID-19 outcomes and modeled the impact of increasing uptake (up to 70% coverage) of bivalent COVID-19 vaccines and nirmatrelvir-ritonavir during acute illness in different risk groups. Risk groups were defined by age (≥50, ≥65, ≥75 years) and vaccination status (everyone, primary series only, previously vaccinated). We predicted the number of averted COVID-19 cases, hospitalizations, and deaths and number needed to treat (NNT). Results: The model predicted that increased uptake of bivalent COVID-19 boosters and nirmatrelvir-ritonavir (up to 70% coverage) in all eligible persons could avert an estimated 15.7% (95% uncertainty interval [UI], 11.2%-20.7%; NNT: 17 310) and 23.5% (95% UI, 13.1%-30.0%; NNT: 67) of total COVID-19-related deaths, respectively. In the high-risk group of persons ≥65 years old alone, increased uptake of bivalent boosters and nirmatrelvir-ritonavir could avert an estimated 11.9% (95% UI, 8.4%-15.1%; NNT: 2757) and 22.8% (95% UI, 12.7%-29.2%; NNT: 50) of total COVID-19-related deaths, respectively. Conclusions: These findings suggest that prioritizing uptake of bivalent boosters and nirmatrelvir-ritonavir among older age groups (≥65 years) would be most effective (based on NNT) but would not address the entire burden of severe COVID-19.

Application of a life table approach to assess duration of BNT162b2 vaccine-derived immunity by age using COVID-19 case surveillance data during the Omicron variant period.

BACKGROUND: SARS-CoV-2 Omicron variants have the potential to impact vaccine effectiveness and duration of vaccine-derived immunity. We analyzed U.S. multi-jurisdictional COVID-19 vaccine breakthrough surveillance data to examine potential waning of protection against SARS-CoV-2 infection for the Pfizer-BioNTech (BNT162b) primary vaccination series by age. METHODS: Weekly numbers of SARS-CoV-2 infections during January 16, 2022-May 28, 2022 were analyzed by age group from 22 U.S. jurisdictions that routinely linked COVID-19 case surveillance and immunization data. A life table approach incorporating line-listed and aggregated COVID-19 case datasets with vaccine administration and U.S. Census data was used to estimate hazard rates of SARS-CoV-2 infections, hazard rate ratios (HRR) and percent reductions in hazard rate comparing unvaccinated people to people vaccinated with a Pfizer-BioNTech primary series only, by age group and time since vaccination. RESULTS: The percent reduction in hazard rates for persons 2 weeks after vaccination with a Pfizer-BioNTech primary series compared with unvaccinated persons was lowest among children aged 5-11 years at 35.5% (95% CI: 33.3%, 37.6%) compared to the older age groups, which ranged from 68.7%-89.6%. By 19 weeks after vaccination, all age groups showed decreases in the percent reduction in the hazard rates compared with unvaccinated people; with the largest declines observed among those aged 5-11 and 12-17 years and more modest declines observed among those 18 years and older. CONCLUSIONS: The decline in vaccine protection against SARS-CoV-2 infection observed in this study is consistent with other studies and demonstrates that national case surveillance data were useful for assessing early signals in age-specific waning of vaccine protection during the initial period of SARS-CoV-2 Omicron variant predominance. The potential for waning immunity during the Omicron period emphasizes the importance of continued monitoring and consideration of optimal timing and provision of booster doses in the future.

Correlation between wastewater and COVID-19 case incidence rates in major California sewersheds across three variant periods.

Monitoring for COVID-19 through wastewater has been used for adjunctive public health surveillance, with SARS-CoV-2 viral concentrations in wastewater correlating with incident cases in the same sewershed. However, the generalizability of these findings across sewersheds, laboratory methods, and time periods with changing variants and underlying population immunity has not been well described. The California Department of Public Health partnered with six wastewater treatment plants starting in January 2021 to monitor wastewater for SARS-CoV-2, with analyses performed at four laboratories. Using reported PCR-confirmed COVID-19 cases within each sewershed, the relationship between case incidence rates and wastewater concentrations collected over 14 months was evaluated using Spearman's correlation and linear regression. Strong correlations were observed when wastewater concentrations and incidence rates were averaged (10- and 7-day moving window for wastewater and cases, respectively, ρ = 0.73-0.98 for N1 gene target). Correlations remained strong across three time periods with distinct circulating variants and vaccination rates (winter 2020-2021/Alpha, summer 2021/Delta, and winter 2021-2022/Omicron). Linear regression revealed that slopes of associations varied by the dominant variant of concern, sewershed, and laboratory (ß = 0.45-1.94). These findings support wastewater surveillance as an adjunctive public health tool to monitor SARS-CoV-2 community trends.

Trends in Laboratory-Confirmed SARS-CoV-2 Reinfections and Associated Hospitalizations and Deaths Among Adults Aged ≥18 Years - 18 U.S. Jurisdictions, September 2021-December 2022.

Although reinfections with SARS-CoV-2 have occurred in the United States with increasing frequency, U.S. epidemiologic trends in reinfections and associated severe outcomes have not been characterized. Weekly counts of SARS-CoV-2 reinfections, total infections, and associated hospitalizations and deaths reported by 18 U.S. jurisdictions during September 5, 2021-December 31, 2022, were analyzed overall, by age group, and by five periods of SARS-CoV-2 variant predominance (Delta and Omicron [BA.1, BA.2, BA.4/BA.5, and BQ.1/BQ.1.1]). Among reported reinfections, weekly trends in the median intervals between infections and frequencies of predominant variants during previous infections were calculated. As a percentage of all infections, reinfections increased substantially from the Delta (2.7%) to the Omicron BQ.1/BQ.1.1 (28.8%) periods; during the same periods, increases in the percentages of reinfections among COVID-19-associated hospitalizations (from 1.9% [Delta] to 17.0% [Omicron BQ.1/BQ.1.1]) and deaths (from 1.2% [Delta] to 12.3% [Omicron BQ.1/BQ.1.1]) were also substantial. Percentages of all COVID-19 cases, hospitalizations, and deaths that were reinfections were consistently higher across variant periods among adults aged 18-49 years compared with those among adults aged ≥50 years. The median interval between infections ranged from 269 to 411 days by week, with a steep decline at the start of the BA.4/BA.5 period, when >50% of reinfections occurred among persons previously infected during the Alpha variant period or later. To prevent severe COVID-19 outcomes, including those following reinfection, CDC recommends staying up to date with COVID-19 vaccination and receiving timely antiviral treatments, when eligible.

Predicting the public health impact of bivalent vaccines and nirmatrelvir-ritonavir against COVID-19.

Background: Uptake of COVID-19 bivalent vaccines and oral medication nirmatrelvir-ritonavir (Paxlovid) has remained low across the United States. Assessing the public health impact of increasing uptake of these interventions in key risk groups can guide further public health resources and policy. Methods: This modeling study used person-level data from the California Department of Public Health on COVID-19 cases, hospitalizations, deaths, and vaccine administration from July 23, 2022 to January 23, 2023. We modeled the impact of additional uptake of bivalent COVID-19 vaccines and nirmatrelvir-ritonavir during acute illness in different risk groups defined by age (50+, 65+, 75+ years) and vaccination status (everyone, primary series only, previously vaccinated). We predicted the number of averted COVID-19 cases, hospitalizations, and deaths and number needed to treat (NNT). Results: For both bivalent vaccines and nirmatrelvir-ritonavir, the most efficient strategy (based on NNT) for averting severe COVID-19 was targeting the 75+ years group. We predicted that perfect coverage of bivalent boosters in the 75+ years group would avert 3,920 hospitalizations (95%UI: 2,491-4,882; 7.8% total averted; NNT 387) and 1,074 deaths (95%UI: 774-1,355; 16.2% total averted; NNT 1,410). Perfect uptake of nirmatrelvir-ritonavir in the 75+ years group would avert 5,644 hospitalizations (95%UI: 3,947-6,826; 11.2% total averted; NNT 11) and 1,669 deaths (95%UI: 1,053-2,038; 25.2% total averted; NNT 35). Conclusions: These findings suggest prioritizing uptake of bivalent boosters and nirmatrelvir-ritonavir among the oldest age groups would be efficient and have substantial public health impact in reducing the burden of severe COVID-19, but would not address the entire burden of severe COVID-19.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making.

BACKGROUND: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. METHODS: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. RESULTS: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. CONCLUSIONS: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

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.

Risk factor targeting for vaccine prioritization during the COVID-19 pandemic.

A key public health question during any disease outbreak when limited vaccine is available is who should be prioritized for early vaccination. Most vaccine prioritization analyses only consider variation in risk of infection and death by a single risk factor, such as age. We provide a more granular approach with stratification by demographics, risk factors, and location. We use this approach to compare the impact of different COVID-19 vaccine prioritization strategies on COVID-19 cases, deaths and disability-adjusted life years (DALYs) over the first 6 months of vaccine rollout, using California as a case example. We estimate the proportion of cases, deaths and DALYs averted relative to no vaccination for strategies prioritizing vaccination by a single risk factor and by multiple risk factors (e.g. age, location). When targeting by a single risk factor, we find that age-based targeting averts the most deaths (62% for 5 million individuals vaccinated) and DALYs (38%) and targeting essential workers averts the least deaths (31%) and DALYs (24%) over the first 6 months of rollout. However, targeting by two or more risk factors simultaneously averts up to 40% more DALYs. Our findings highlight the potential value of multiple-risk-factor targeting of vaccination against COVID-19 and other infectious diseases, but must be balanced with feasibility for policy.

COVID-19 Cases and Hospitalizations by COVID-19 Vaccination Status and Previous COVID-19 Diagnosis - California and New York, May-November 2021.

By November 30, 2021, approximately 130,781 COVID-19-associated deaths, one in six of all U.S. deaths from COVID-19, had occurred in California and New York.* COVID-19 vaccination protects against infection with SARS-CoV-2 (the virus that causes COVID-19), associated severe illness, and death (1,2); among those who survive, previous SARS-CoV-2 infection also confers protection against severe outcomes in the event of reinfection (3,4). The relative magnitude and duration of infection- and vaccine-derived protection, alone and together, can guide public health planning and epidemic forecasting. To examine the impact of primary COVID-19 vaccination and previous SARS-CoV-2 infection on COVID-19 incidence and hospitalization rates, statewide testing, surveillance, and COVID-19 immunization data from California and New York (which account for 18% of the U.S. population) were analyzed. Four cohorts of adults aged ≥18 years were considered: persons who were 1) unvaccinated with no previous laboratory-confirmed COVID-19 diagnosis, 2) vaccinated (14 days after completion of a primary COVID-19 vaccination series) with no previous COVID-19 diagnosis, 3) unvaccinated with a previous COVID-19 diagnosis, and 4) vaccinated with a previous COVID-19 diagnosis. Age-adjusted hazard rates of incident laboratory-confirmed COVID-19 cases in both states were compared among cohorts, and in California, hospitalizations during May 30-November 20, 2021, were also compared. During the study period, COVID-19 incidence in both states was highest among unvaccinated persons without a previous COVID-19 diagnosis compared with that among the other three groups. During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; 7.2-fold (California) and 9.9-fold lower (New York) among unvaccinated persons with a previous COVID-19 diagnosis; and 9.6-fold (California) and 8.5-fold lower (New York) among vaccinated persons with a previous COVID-19 diagnosis. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These relationships changed after the SARS-CoV-2 Delta variant became predominant (i.e., accounted for >50% of sequenced isolates) in late June and July. By the week beginning October 3, compared with COVID-19 cases rates among unvaccinated persons without a previous COVID-19 diagnosis, case rates among vaccinated persons without a previous COVID-19 diagnosis were 6.2-fold (California) and 4.5-fold (New York) lower; rates were substantially lower among both groups with previous COVID-19 diagnoses, including 29.0-fold (California) and 14.7-fold lower (New York) among unvaccinated persons with a previous diagnosis, and 32.5-fold (California) and 19.8-fold lower (New York) among vaccinated persons with a previous diagnosis of COVID-19. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These results demonstrate that vaccination protects against COVID-19 and related hospitalization, and that surviving a previous infection protects against a reinfection and related hospitalization. Importantly, infection-derived protection was higher after the Delta variant became predominant, a time when vaccine-induced immunity for many persons declined because of immune evasion and immunologic waning (2,5,6). Similar cohort data accounting for booster doses needs to be assessed, as new variants, including Omicron, circulate. Although the epidemiology of COVID-19 might change with the emergence of new variants, vaccination remains the safest strategy to prevent SARS-CoV-2 infections and associated complications; all eligible persons should be up to date with COVID-19 vaccination. Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

Household-level risk factors for Aedes aegypti pupal density in Guayaquil, Ecuador.

BACKGROUND: Vector-borne diseases are a major cause of disease burden in Guayaquil, Ecuador, especially arboviruses spread by Aedes aegypti mosquitoes. Understanding which household characteristics and risk factors lead to higher Ae. aegypti densities and consequent disease risk can help inform and optimize vector control programs. METHODS: Cross-sectional entomological surveys were conducted in Guayaquil between 2013 and 2016, covering household demographics, municipal services, potential breeding containers, presence of Ae. aegypti larvae and pupae, and history of using mosquito control methods. A zero-truncated negative binomial regression model was fitted to data for estimating the household pupal index. An additional model assessed the factors of the most productive breeding sites across all of the households. RESULTS: Of surveyed households, 610 satisfied inclusion criteria. The final household-level model found that collection of large solid items (e.g., furniture and tires) and rainfall the week of and 2 weeks before collection were negatively correlated with average pupae per container, while bed canopy use, unemployment, container water volume, and the interaction between large solid collection and rainfall 2 weeks before the sampling event were positively correlated. Selection of these variables across other top candidate models with ∆AICc < 1 was robust, with the strongest effects from large solid collection and bed canopy use. The final container-level model explaining the characteristics of breeding sites found that contaminated water is positively correlated with Ae. aegypti pupae counts while breeding sites composed of car parts, furniture, sewerage parts, vases, were all negatively correlated. CONCLUSIONS: Having access to municipal services like bulky item pickup was effective at reducing mosquito proliferation in households. Association of bed canopy use with higher mosquito densities is unexpected, and may be a consequence of large local mosquito populations or due to limited use or effectiveness of other vector control methods. The impact of rainfall on mosquito density is multifaceted, as it may both create new habitat and "wash out" existing habitat. Providing services and social/technical interventions focused on monitoring and eliminating productive breeding sites is important for reducing aquatic-stage mosquito densities in households at risk for Ae. aegypti-transmitted diseases.

Nonpharmaceutical Interventions Remain Essential to Reducing Coronavirus Disease 2019 Burden Even in a Well-Vaccinated Society: A Modeling Study.

Vaccination and nonpharmaceutical interventions (NPIs) reduce transmission of severe acute respiratory syndrome coronavirus 2 infection, but their effectiveness depends on coverage and adherence levels. We used scenario modeling to evaluate their effects on cases and deaths averted and herd immunity. NPIs and vaccines worked synergistically in different parts of the pandemic to reduce disease burden.

MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics.

Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project's CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.

Winning the Tug-of-War Between Effector Gene Design and Pathogen Evolution in Vector Population Replacement Strategies.

While efforts to control malaria with available tools have stagnated, and arbovirus outbreaks persist around the globe, the advent of clustered regularly interspaced short palindromic repeat (CRISPR)-based gene editing has provided exciting new opportunities for genetics-based strategies to control these diseases. In one such strategy, called "population replacement", mosquitoes, and other disease vectors are engineered with effector genes that render them unable to transmit pathogens. These effector genes can be linked to "gene drive" systems that can bias inheritance in their favor, providing novel opportunities to replace disease-susceptible vector populations with disease-refractory ones over the course of several generations. While promising for the control of vector-borne diseases on a wide scale, this sets up an evolutionary tug-of-war between the introduced effector genes and the pathogen. Here, we review the disease-refractory genes designed to date to target Plasmodium falciparum malaria transmitted by Anopheles gambiae, and arboviruses transmitted by Aedes aegypti, including dengue serotypes 2 and 3, chikungunya, and Zika viruses. We discuss resistance concerns for these effector genes, and genetic approaches to prevent parasite and viral escape variants. One general approach is to increase the evolutionary hurdle required for the pathogen to evolve resistance by attacking it at multiple sites in its genome and/or multiple stages of development. Another is to reduce the size of the pathogen population by other means, such as with vector control and antimalarial drugs. We discuss lessons learned from the evolution of resistance to antimalarial and antiviral drugs and implications for the management of resistance after its emergence. Finally, we discuss the target product profile for population replacement strategies for vector-borne disease control. This differs between early phase field trials and wide-scale disease control. In the latter case, the demands on effector gene efficacy are great; however, with new possibilities ushered in by CRISPR-based gene editing, and when combined with surveillance, monitoring, and rapid management of pathogen resistance, the odds are increasingly favoring effector genes in the upcoming evolutionary tug-of-war.

Modeling liver fluke transmission in northeast Thailand: Impacts of development, hydrology, and control.

Human infection with the Southeast Asian liver fluke Opisthorchis viverrini and liver fluke-associated cholangiocarcinoma cause significant disease burden in Southeast Asia. While there has been considerable work to understand liver fluke pathology and to reduce infection prevalence, there remains a limited understanding of the environmental determinants of parasite transmission dynamics to inform treatment and control programs. A particular setting where targeted control efforts have taken place is the Lawa Lake complex in northeast Thailand. Here, we describe the recent history of host infections, as well as the hydrologic characteristics of this floodplain ecosystem that influence the extent of snail habitat and fish mobility and the transport of human waste and parasite cercariae. Using mathematical modeling, we outline a framework for reconstructing environmental transmission of O. viverrini over the course of the Lawa Project control program from its inception in 2008 until 2016, using locally acquired but fragmentary longitudinal infection data for both humans and environmental hosts. The role of water flow in facilitating movement between snail, fish, human, and reservoir hosts is a particular focus with respect to its relevant scales and its impact on success of interventions. In this setting, we argue that an understanding of the key environmental drivers of disease transmission processes is central to the effectiveness of any environmental intervention.