The probability of epidemic burnout in the stochastic SIR model with vital dynamics.

We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.

Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.

Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.

Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.

Sociocultural determinants of global mask-wearing behavior.

Behavioral responses influence the trajectories of epidemics. During the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) reduced pathogen transmission and mortality worldwide. However, despite the global pandemic threat, there was substantial cross-country variation in the adoption of protective behaviors that is not explained by disease prevalence alone. In particular, many countries show a pattern of slow initial mask adoption followed by sharp transitions to high acceptance rates. These patterns are characteristic of behaviors that depend on social norms or peer influence. We develop a game-theoretic model of mask wearing where the utility of wearing a mask depends on the perceived risk of infection, social norms, and mandates from formal institutions. In this model, increasing pathogen transmission or policy stringency can trigger social tipping points in collective mask wearing. We show that complex social dynamics can emerge from simple individual interactions and that sociocultural variables and local policies are important for recovering cross-country variation in the speed and breadth of mask adoption. These results have implications for public health policy and data collection.

Bacterial Mutation During Seasonal Epidemics.

Rapidly evolving bacterial pathogens pose a unique challenge for long-term plant disease management. In this study, we investigated the types and rate of mutations in bacterial populations during seasonal disease epidemics. Two phylogenetically distinct strains of the bacterial spot pathogen, Xanthomonas perforans, were marked, released in tomato fields, and recaptured at several time points during the growing season. Genomic variations in recaptured isolates were identified by comparative analysis of their whole-genome sequences. In total, 180 unique variations (116 substitutions, 57 insertions/deletions, and 7 structural variations) were identified from 300 genomes, resulting in the overall host-associated mutation rate of ∼0.3 to 0.9/genome/week. This result serves as a benchmark for bacterial mutation during epidemics in similar pathosystems. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Vaccine-Preventable Disease Outbreaks among Healthcare Workers: A Scoping Review.

BACKGROUND: Outbreaks of vaccine preventable diseases (VPDs) in health care workers (HCWs) can result in morbidity and mortality and cause significant disruptions to health care services, patients and visitors as well as an added burden on the health system. This scoping review is aimed to describe the epidemiology of VPD outbreaks in HCW, caused by diseases which are prevented by the ten vaccines recommended by World Health Organization (WHO) for HCWs. METHODS: In April 2022 CINAHL, MEDLINE, Global Health and EMBASE were searched for all articles reporting on VPD outbreaks in HCWs since the year 2000. Articles were included regardless of language and study type. Clinical and epidemiological characteristics of VPD outbreaks were described. RESULTS: Our search found 9363 articles, of which 216 met inclusion criteria. Studies describing six of the ten VPDs were found: influenza, measles, varicella, tuberculosis, pertussis and rubella. Most articles (93%) were from high- and upper middle-income countries. While most outbreaks occurred in hospitals, several influenza outbreaks were reported in long term care facilities. Based on available data, vaccination rates amongst HCWs were rarely reported. CONCLUSION: We describe several VPD outbreaks in HCWs from 2000 to April 2022. The review emphasises the need to understand the factors influencing outbreaks in HCWs and highlight importance of vaccination amongst HCWs.

Systematic Review of Scales for Measuring Infectious Disease-Related Stigma.

Infectious disease outbreaks are associated with substantial stigma, which can have negative effects on affected persons and communities and on outbreak control. Thus, measuring stigma in a standardized and validated manner early in an outbreak is critical to disease control. We reviewed existing scales used to assess stigma during outbreaks. Our findings show that many different scales have been developed, but few have been used more than once, have been adequately validated, or have been tested in different disease and geographic contexts. We found that scales were usually developed too slowly to be informative early during an outbreak and were published a median of 2 years after the first case of an outbreak. A rigorously developed, transferable stigma scale is needed to assess and direct responses to stigma during infectious disease outbreaks.

Enterovirus D68 disease burden and epidemiology in hospital-admitted influenza-like illness, Valencia region of Spain, 2014-2020 influenza seasons.

Enterovirus D68 (EV-D68) is an emerging agent for which data on the susceptible adult population is scarce. We performed a 6-year analysis of respiratory samples from influenza-like illness (ILI) admitted during 2014-2020 in 4-10 hospitals in the Valencia Region, Spain. EV-D68 was identified in 68 (3.1%) among 2210 Enterovirus (EV)/Rhinovirus (HRV) positive samples. Phylogeny of 59 VP1 sequences showed isolates from 2014 clustering in B2 (6/12), B1 (5/12), and A2/D1 (1/12) subclades; those from 2015 (n = 1) and 2016 (n = 1) in B3 and A2/D1, respectively; and isolates from 2018 in A2/D3 (42/45), and B3 (3/45). B1 and B2 viruses were mainly detected in children (80% and 67%, respectively); B3 were equally distributed between children and adults; whereas A2/D1 and A2/D3 were observed only in adults. B3 viruses showed up to 16 amino acid changes at predicted antigenic sites. In conclusion, two EV-D68 epidemics linked to ILI hospitalized cases occurred in the Valencia Region in 2014 and 2018, with three fatal outcomes and one ICU admission. A2/D3 strains from 2018 were associated with severe respiratory infection in adults. Because of the significant impact of non-polio enteroviruses in ILI and the potential neurotropism, year-round surveillance in respiratory samples should be pursued.

Cocirculation and coinfection of multiple respiratory viruses during autumn and winter seasons of 2023 in Beijing, China: A retrospective study.

China experienced severe epidemics of multiple respiratory pathogens in 2023 after lifting "Zero-COVID" policy. The present study aims to investigate the changing circulation and infection patterns of respiratory pathogens in 2023. The 160 436 laboratory results of influenza virus and respiratory syncytial virus (RSV) from February 2020 to December 2023, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from June 2020 to December 2023, Mycoplasma pneumoniae, adenovirus, and human rhinovirus from January 2023 to December 2023 were analyzed. We observed the alternating epidemics of SARS-CoV-2 and influenza A virus (IAV), as well as the out-of-season epidemic of RSV during the spring and summer of 2023. Cocirculation of multiple respiratory pathogens was observed during the autumn and winter of 2023. The susceptible age range of RSV in this winter epidemic (10.5, interquartile range [IQR]: 5-30) was significantly higher than previously (4, IQR: 3-34). The coinfection rate of IAV and RSV in this winter epidemic (0.695%) was significantly higher than that of the last cocirculation period (0.027%) (p < 0.001). Similar trend was also found in the coinfection of IAV and SARS-CoV-2. The present study observed the cocirculation of multiple respiratory pathogens, changing age range of susceptible population, and increasing coinfection rates during the autumn and winter of 2023, in Beijing, China.

Wastewater-borne viruses and bacteria, surveillance and biosensors at the interface of academia and field deployment.

Wastewater is a complex, but an ideal, matrix for disease monitoring and surveillance as it represents the entire load of enteric pathogens from a local catchment area. It captures both clinical and community disease burdens. Global interest in wastewater surveillance has been growing rapidly for infectious diseases monitoring and for providing an early warning of potential outbreaks. Although molecular detection methods show high sensitivity and specificity in pathogen monitoring from wastewater, they are strongly limited by challenges, including expensive laboratory settings and prolonged sample processing and analysis. Alternatively, biosensors exhibit a wide range of practical utility in real-time monitoring of biological and chemical markers. However, field deployment of biosensors is primarily challenged by prolonged sample processing and pathogen concentration steps due to complex wastewater matrices. This review summarizes the role of wastewater surveillance and provides an overview of infectious viral and bacterial pathogens with cutting-edge technologies for their detection. It emphasizes the practical utility of biosensors in pathogen monitoring and the major bottlenecks for wastewater surveillance of pathogens, and overcoming approaches to field deployment of biosensors for real-time pathogen detection. Furthermore, the promising potential of novel machine learning algorithms to resolve uncertainties in wastewater data is discussed.

The epidemiological profile of incidence and mortality from epidemics in complex humanitarian emergencies from 1990 to 2022 - A scoping review.

AIM: This study aimed to investigate the impact of communicable diseases with epidemic potential in complex emergency (CE) situations, focusing on the epidemiological profile of incidence and mortality and exploring underlying factors contributing to increased epidemic risks. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines, we conducted a scoping review of articles published between 1990 and 2022. The search included terms related to complex emergencies, communicable diseases, outbreaks, and epidemics. We identified 92 epidemics related to CE occurring in 32 different countries. RESULTS: Communicable diseases like Shigellosis, Cholera, Measles, Meningococcal meningitis, Yellow Fever, and Malaria caused significant morbidity and mortality. Diarrhoeal diseases, particularly Cholera and Shigellosis, had the highest incidence rates. Shigella specifically had an incidence of 241.0 per 1000 (people at risk), with a mortality rate of 11.7 per 1000, while Cholera's incidence was 13.0 per 1000, with a mortality rate of 0.22 per 1000. Measles followed, with an incidence of 25.0 per 1000 and a mortality rate of 0.76 per 1000. Meningococcal Meningitis had an incidence rate of 1.3 per 1000 and a mortality rate of 0.13 per 1000. Despite their lower incidences, yellow fever at 0.8 per 1000 and malaria at 0.4 per 1000, their high case fatality rates of 20.1% and 0.4% remained concerning in CE. The qualitative synthesis reveals that factors such as water, sanitation, and hygiene, shelter and settlements, food and nutrition, and public health and healthcare in complex emergencies affect the risk of epidemics. CONCLUSION: Epidemics during complex emergencies could potentially lead to a public health crisis. Between 1990 and 2022, there have been no statistically significant changes in the trend of incidence, mortality, or fatality rates of epidemic diseases in CE. It is crucial to understand that all epidemics identified in CE are fundamentally preventable.

Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic.

The overall course of the COVID-19 pandemic in Western countries has been characterized by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strengthening/lifting of social distancing measures, with the aim to balance the protection of health and that of the society as a whole. After the arrival of vaccines, this multi-phasic character was further emphasized by the complicated deployment of vaccination campaigns and the onset of virus' variants. To cope with this multi-phasic character, we propose a theoretical approach to the modeling of overall pandemic courses, that we term multi-period/multi-phasic, based on a specific definition of phase. This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus' variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggest a simple, theory driven, approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning (February 2020) up to omicron onset, confirming the validity of the concept.

Adaptive sequential surveillance with network and temporal dependence.

Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.

Dynamics of viral disease outbreaks: A hundred years (1918/19-2019/20) in retrospect - Loses, lessons and emerging issues.

Infectious diseases continue to be the leading cause of morbidity and mortality, and a formidable obstacle to the development and well-being of people worldwide. Viruses account for more than half of infectious disease outbreaks that have plagued the world. The past century (1918/19-2019/20) has witnessed some of the worst viral disease outbreaks the world has recorded, with overwhelming impact especially in low- and middle-income countries (LMIC). The frequency of viral disease outbreak appears to be increasing. Generally, although infectious diseases have afflicted the world for centuries and humankind has had opportunities to examine the nature of their emergence and mode of spread, almost every new outbreak poses a formidable challenge to humankind, beating the existing pandemic preparedness systems, if any, and causing significant losses. These underscore inadequacy in our understanding of the dynamics and preparedness against viral disease outbreaks that lead to epidemics and pandemics. Despite these challenges, the past 100 years of increasing frequencies of viral disease outbreaks have engendered significant improvements in response to epidemics and pandemics, and offered lessons to inform preparedness. Hence, the increasing frequency of emergence of viral outbreaks and the challenges these outbreaks pose to humankind, call for the continued search for effective ways to tackle viral disease outbreaks in real time. Through a PRISMA-based approach, this systematic review examines the outbreak of viral diseases in retrospect to decipher the outbreak patterns, losses inflicted on humanity and highlights lessons these offer for meaningful preparation against future viral disease outbreaks and pandemics.

Reconstruction and analysis of the transmission network of African swine fever in People's Republic of China, August 2018-September 2019.

Introduction of African swine fever (ASF) to China in mid-2018 and the subsequent transboundary spread across Asia devastated regional swine production, affecting live pig and pork product-related markets worldwide. To explore the spatiotemporal spread of ASF in China, we reconstructed possible ASF transmission networks using nearest neighbour, exponential function, equal probability, and spatiotemporal case-distribution algorithms. From these networks, we estimated the reproduction numbers, serial intervals, and transmission distances of the outbreak. The mean serial interval between paired units was around 29 days for all algorithms, while the mean transmission distance ranged 332 -456 km. The reproduction numbers for each algorithm peaked during the first two weeks and steadily declined through the end of 2018 before hovering around the epidemic threshold value of 1 with sporadic increases during 2019. These results suggest that 1) swine husbandry practices and production systems that lend themselves to long-range transmission drove ASF spread; 2) outbreaks went undetected by the surveillance system. Efforts by China and other affected countries to control ASF within their jurisdictions may be aided by the reconstructed spatiotemporal model. Continued support for strict implementation of biosecurity standards and improvements to ASF surveillance is essential for halting transmission in China and spread across Asia.

Spatiotemporal dynamics of cholera hotspots in the Democratic Republic of the Congo from 1973 to 2022.

BACKGROUND: Since the early 1970s, cholera outbreaks have been a major public health burden in the Democratic Republic of Congo (DRC). Cholera cases have been reported in a quasi-continuous manner in certain lakeside areas in the Great Lakes Region. As these cholera-endemic health zones constitute a starting point for outbreaks and diffusion towards other at-risk areas, they play a major role in cholera dynamics in the country. Monitoring the spatiotemporal dynamics of cholera hotspots and adjusting interventions accordingly thus reduces the disease burden in an efficient and cost-effective manner. METHODS: A literature review was conducted to describe the spatiotemporal dynamics of cholera in the DRC at the province level from 1973 to 1999. We then identified and classified cholera hotspots at the provincial and health zone levels from 2003 to 2022 and described the spatiotemporal evolution of hotspots. We also applied and compared three different classification methods to ensure that cholera hotspots are identified and classified according to the DRC context. RESULTS: According to all three methods, high-priority hotspots were concentrated in the eastern Great Lakes Region. Overall, hotspots largely remained unchanged over the course of the study period, although slight improvements were observed in some eastern hotspots, while other non-endemic areas in the west experienced an increase in cholera outbreaks. The Global Task Force on Cholera Control (GTFCC) and the Department of Ecology and Infectious Disease Control (DEIDC) methods largely yielded similar results for the high-risk hotspots. However, the medium-priority hotspots identified by the GTFCC method were further sub-classified by the DEIDC method, thereby providing a more detailed ranking for priority targeting. CONCLUSIONS: Overall, the findings of this comprehensive study shed light on the dynamics of cholera hotspots in the DRC from 1973 to 2022. These results may serve as an evidence-based foundation for public health officials and policymakers to improve the implementation of the Multisectoral Cholera Elimination Plan, guiding targeted interventions and resource allocation to mitigate the impact of cholera in vulnerable communities.

An Agent-Based Model Shows How Mixed Infections Drive Multiyear Pathotype Dynamics in a Plant-Virus System.

Mathematical models are widely used to understand the evolution and epidemiology of plant pathogens under a variety of scenarios. Here, we used this approach to analyze the effects of different traits intrinsic and extrinsic to plant-virus interactions on the dynamics of virus pathotypes in genetically heterogeneous plant-virus systems. For this, we propose an agent-based epidemiological model that includes epidemiologically significant pathogen life-history traits related to virulence, transmission, and survival in the environment and allows for integrating long- and short-distance transmission, primary and secondary infections, and within-host pathogen competition in mixed infections. The study focuses on the tobamovirus-pepper pathosystem. Model simulations allowed us to integrate pleiotropic effects of resistance-breaking mutations on different virus life-history traits into the net costs of resistance breaking, allowing for predictions on multiyear pathotype dynamics. We also explored the effects of two control measures, the use of host resistance and roguing of symptomatic plants, that modify epidemiological attributes of the pathogens to understand how their populations will respond to evolutionary pressures. One major conclusion points to the importance of pathogen competition within mixed-infected hosts as a component of the overall fitness of each pathogen that, thus, drives their multiyear dynamics.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number R 0 < 1 . For R 0 > 1 , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

Mental health preparedness and response to epidemics focusing on COVID-19 pandemic: a qualitative study in Iran.

BACKGROUND: During epidemics, the number of individuals whose mental health is affected is greater than those affected by the infection itself. This is because psychological factors have a direct relationship with the primary causes of the disease and mortality worldwide. Therefore, an increasing investment in research and strategic actions for mental health is essential globally, given the prevalence of infectious diseases. The aim of this study was to elucidate and describe the strategies for mental health preparedness and response during epidemics, with a focus on the COVID-19 pandemic in Iran. METHODS: A qualitative study was conducted in Iran from 2022 to 2023. Purposeful Sampling was employed, continuing until data saturation was achieved. Data collection involved semi-structured interviews and observational notes with 20 managers and experts possessing expertise, experience, and knowledge in mental health. Ultimately, the participants' opinions, based on their experiences, were analyzed using the qualitative content analysis method with a conventional approach, resulting in the categorization of data into codes, subcategories, and categories. RESULTS: The study revealed participants' opinions and experiences, categorized into two overarching categories: Preparedness, Policy-Making, and Planning Strategies (with four subcategories), and Response Strategies (comprising thirteen subcategories). CONCLUSION: The opinions and experiences of managers and experts in this study revealed that an appropriate mental health response during pandemics requires preparedness before the occurrence of such crises and the implementation of suitable response strategies after the occurrence. Managers, policymakers, and decision-makers in this field should pay attention to the solutions derived from the experiences of such crises to respond more preparedly in the future.

Health and non-health benefits and equity impacts of individual-level economic relief programs during epidemics/pandemics in high income settings: a scoping review.

BACKGROUND: Economic relief programs are strategies designed to sustain societal welfare and population health during a regional or global scale infectious disease outbreak. While economic relief programmes are considered essential during a regional or global health crisis, there is no clear consensus in the literature about their health and non-health benefits and their impact on promoting equity. METHODS: We conducted a scoping review, searching eight electronic databases from January 01, 2001, to April 3, 2023, using text words and subject headings for recent pathogens (coronavirus (COVID-19), Ebola, Influenza, Middle East Respiratory Syndrome (MERS), severe acute respiratory syndrome (SARS), HIV, West Nile, and Zika), and economic relief programs; but restricted eligibility to high-income countries and selected diseases due to volume. Title and abstract screening were conducted by trained reviewers and Distiller AI software. Data were extracted in duplicates by two trained reviewers using a pretested form, and key findings were charted using a narrative approach. RESULTS: We identified 27,263 de-duplicated records, of which 50 were eligible. Included studies were on COVID-19 and Influenza, published between 2014 and 2023. Zero eligible studies were on MERS, SARS, Zika, Ebola, or West Nile Virus. We identified seven program types of which cash transfer (n = 12) and vaccination or testing incentive (n = 9) were most common. Individual-level economic relief programs were reported to have varying degrees of impact on public health measures, and sometimes affected population health outcomes. Expanding paid sick leave programs had the highest number of studies reporting health-related outcomes and positively impacted public health measures (isolation, vaccination uptake) and health outcomes (case counts and the utilization of healthcare services). Equity impact was most often reported for cash transfer programs and incentive for vaccination programs. Positive effects on general well-being and non-health outcomes included improved mental well-being and quality of life, food security, financial resilience, and job security. CONCLUSIONS: Our findings suggest that individual-level economic relief programs can have significant impacts on public health measures, population health outcomes and equity. As countries prepare for future pandemics, our findings provide evidence to stakeholders to recognize health equity as a fundamental public health goal when designing pandemic preparedness policies.