Economic evaluations of interventions against influenza at workplaces: systematic review.

BACKGROUND: The burden of influenza is mostly felt by employees and employers because of increased absenteeism rates, loss of productivity and associated direct costs. Even though interventions against influenza among working adults are effective, patronage and compliance to these measures especially vaccination are low compared to other risk groups. AIMS: This study was aimed to assess evidence of economic evaluations of interventions against influenza virus infection among workers or in the workplace setting. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reporting guideline for systematic reviews was followed. Three databases, PubMed, Web of Science and EconLit, were searched using keywords to identify relevant articles from inception till 25 October 2020. Original peer-reviewed papers that conducted economic evaluations of influenza interventions using cost-benefit, cost-effectiveness or cost-utility analysis methods focused on working-age adults or work settings were eligible for inclusion. Two independent teams of co-authors extracted and synthesized data from identified studies. RESULTS: Twenty-four articles were included: 21 were cost-benefit analyses and 3 examined cost-effectiveness analyses. Two papers also presented additional cost-utility analysis. Most of the studies were pharmaceutical interventions (n = 23) primarily focused on vaccination programs while one study was a non-pharmaceutical intervention examining the benefit of paid sick leave. All but two studies reported that interventions against influenza virus infection at the workplace were cost-saving and cost-effective regardless of the analytic approach. CONCLUSIONS: Further cost-effectiveness research in non-pharmaceutical interventions against influenza in workplace settings is warranted. There is a need to develop standardized methods for reporting economic evaluation methods to ensure comparability and applicability of future research findings.

Spatial variability in the risk of death from COVID-19 in Italy.

OBJECTIVES: Italy has been badly affected by the COVID-19 pandemic and has one of the highest death tolls. We analyzed the severity of COVID-19 across all 20 Italian regions.METHOD: We manually retrieved the daily cumulative numbers of laboratory-confirmed cases and deaths attributed to COVID-19 in each region, and estimated the crude case fatality ratio and time delay-adjusted case fatality ratio (aCFR). We then assessed the association between aCFR and sociodemographic, health care and transmission factors using multivariate regression analysis.RESULTS: The overall aCFR in Italy was estimated at 17.4%. Lombardia exhibited the highest aCFR (24.7%), followed by Marche (19.3%), Emilia Romagna (17.7%) and Liguria (17.6%). Our aCFR estimate was greater than 10% for 12 regions. Our aCFR estimates were statistically associated with population density and cumulative morbidity rate in a multivariate analysis.CONCLUSION: Our aCFR estimates for Italy as a whole and for seven out of the 20 regions exceeded those reported for the most badly affected region in China. These findings highlight the importance of social distancing to suppress transmission to avoid overwhelming the health care system and reduce the risk of death.

Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020.

The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409-7496 additional confirmed cases in Hubei and 1128-1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588-13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th - 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.

Susceptibility to organophosphates pesticides and the development of infectious-contagious respiratory diseases.

In this paper we develop an SIRS compartmental model to investigate the dynamic interplay between pesticide intoxication and the spread of infectious-contagious respiratory diseases. We are particularly interested in investigating three levels of genetic susceptibility to pesticide intoxication. The genotypic distribution of susceptibility to pesticide intoxication, is proposed and parameterized according to ethnic variation using real population data from published studies, and we assume that pesticide intoxication increases susceptibility to infection with a respiratory pathogen. We use mathematical models to illustrate the impact of this distribution on the spread of hypothetical respiratory disease in a population exposed to the organophosphate pesticide. In this context, we show how an initial basic reproductive number below the epidemic threshold of 1.0 could be enhanced to support epidemic outbreaks in agricultural populations that employ chlorpyrifos pesticides. We further illustrate our modeling framework to study the effect of ethnic group variation in Singapore (Malay, Indian and Chinese) using genetic distribution data from published studies.

Real-time forecasting of epidemic trajectories using computational dynamic ensembles.

Forecasting the trajectory of social dynamic processes, such as the spread of infectious diseases, poses significant challenges that call for methods that account for data and model uncertainty. Here we introduce an ensemble model for sequential forecasting that weights a set of plausible models and use a frequentist computational bootstrap approach to evaluate its uncertainty. We demonstrate the feasibility of our approach using simple dynamic differential-equation models and the trajectory of outbreak scenarios of the Ebola Forecasting Challenge. Specifically, we generate sequential short-term forecasts of epidemic outbreaks by combining phenomenological models that incorporate flexible epidemic growth scaling, namely the Generalized-Growth Model (GGM) and the Generalized Logistic Model (GLM). We rely on the root-mean-square error (RMSE) to quantify the quality of the models' fits during the calibration periods for weighting their contribution to the ensemble model while forecasting performance was evaluated using the RMSE of the forecasts. For a given forecasting horizon (1-4 weeks), we report the performance for each model as the percentage of the number of times each model outperforms the other models. The overall mean RMSE performance of the GLM and the GGM-GLM ensemble models outcompeted that of participant models of the Ebola Forecasting Challenge. We also found that the ensemble model provided more accurate forecasts with higher frequency than the GGM and GLM models, but its performance varied across forecasting horizons. For instance, across all of the Ebola Challenge Scenarios, the ensemble model outperformed the other models at horizons of 2 and 3 weeks while the GLM outperformed other models at horizons of 1 and 4 weeks.

Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework.

Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission in temperate areas of the world such as the southern coastal areas of the USA. In particular, we explore the impact of the timing of HREs relative to the transmission season via analyses that test the sensitivity of HRE-induced epidemics to variation in the effects of rainfall on the dynamics of mosquito breeding capacity, and the intensity and temporal profile of human population displacement patterns. The recent Hurricane Harvey in Texas motivates the simulations reported. Overall, we find that the impact of vector-borne disease transmission is likely to be greater the earlier the HREs occur in the transmission season. Simulations based on data for Hurricane Harvey suggest that the limited impact it had on vector-borne disease transmission was in part because of when it occurred (late August) relative to the local transmission season, and in part because of the mitigating effect of the displacement of people. We also highlight key data gaps related to models of vector-borne disease transmission in the context of natural disasters. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Assessing reporting delays and the effective reproduction number: The Ebola epidemic in DRC, May 2018-January 2019.

On August 1, 2018, the Democratic Republic of Congo declared its 10th and largest outbreak of Ebola inflicting North Khivu and Ituri provinces. The spread of Ebola to Congolese urban centers along with deliberate attacks on the health care workers has hindered epidemiological surveillance activities, leading to substantial reporting delays. Reporting delays distort the epidemic incidence pattern misrepresenting estimates of epidemic potential and the outbreak trajectory. To assess the impact of reporting delays, we conducted a real-time analysis of the dynamics of the ongoing Ebola outbreak in the DRC using epidemiological data retrieved from the World Health Organization Situation Reports and Disease Outbreak News. We analyzed temporal trends in reporting delays, epidemic curves of crude and reporting-delay adjusted incidences and changes in the effective reproduction number, Rt. As of January 15, 2019, 663 Ebola cases have been reported in the Democratic Republic of Congo. The average reporting delay exhibited 81.1% decline from a mean of 17.4 weeks (95% CI 13-24.1) in May, 2018 to 3.3 weeks (95% CI 2.7-4.2) in September, 2018 (F-test statistic = 44.9, p = 0.0067). The Ebola epidemic has shown a two-wave pattern with the first surge in cases occurring between July 30 and August 13, 2018 and the second on September 24, 2018. During the last 4 generation intervals, the trend in the mean Rt has exhibited a slight decline (rho = -0.37, p < 0.001), fluctuating around 0.9 (range: 0-1.8). Our most recent estimate of R is at 0.9 (95% CI: 0.4, 1.1) during the last generation interval. Our most recent analysis of the Ebola outbreak in DRC indicates that the Ebola virus still active although transmission is characterized by a low fluctuating reproduction number. Yet, this pattern does not imply that the epidemic can be easily controlled particularly in the context of unstable epidemiological surveillance efforts hindered by unpredictable local violence.

Spatial infectious disease epidemiology: on the cusp.

Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.

Growth scaling for the early dynamics of HIV/AIDS epidemics in Brazil and the influence of socio-demographic factors.

The early dynamics of an infectious disease outbreak can be affected by various factors including the transmission mode of the disease and host-specific factors. While recent works have highlighted the presence of sub-exponential growth patterns during the early phase of epidemics, empirical studies examining the contribution of different factors to early epidemic growth dynamics are lacking. Here we aim to characterize and explain the early incidence growth patterns of local HIV/AIDS epidemics in Brazil as a function of socio-demographic factors. For this purpose, we accessed annual AIDS incidence series and state-level socio-demographic variables from publicly available databases. To characterize the early growth dynamics of the HIV/AIDS epidemic, we employed the generalized-growth model to estimate with quantified uncertainty the scaling of growth parameter (p) which captures growth patterns ranging from constant incidence (p=0) to sub-exponential (0 < p < 1) and exponential growth dynamics (p=1) at three spatial scales: national, regional, and state levels. We evaluated the relationship between socio-demographic variables and epidemic growth patterns across 27 Brazilian states using mixed-effect regression analyses. We found wide variation in the early dynamics of the AIDS epidemic in Brazil, displaying sub-exponential growth patterns with the p parameter estimated substantially below 1.0. The mean p was estimated to be 0.81 at the national level, with a range of 0.72-0.85 at the regional level, and a range of 0.28-0.96 at the state level. Our findings support the notion that socio-demographic factors contribute to shaping the early growth dynamics of the epidemic at the local level. Gini index and socio-demographic index were negatively associated with the parameter p, whereas urbanicity was positively associated with p. The results could have theoretical significance in understanding differences in growth scaling across different sexually transmitted disease systems, and have public health implications to guide control.

Assessing the risk of observing multiple generations of Middle East respiratory syndrome (MERS) cases given an imported case.

To guide risk assessment, expected numbers of cases and generations were estimated, assuming a case importation of Middle East respiratory syndrome (MERS). Our analysis of 36 importation events yielded the risk of observing secondary transmission events at 22.7% (95% confidence interval: 19.3­25.1). The risks of observing generations 2, 3 and 4 were estimated at 10.5%, 6.1% and 3.9%, respectively. Countries at risk should be ready for highly variable outcomes following an importation of MERS.

Transmission dynamics and control of Ebola virus disease outbreak in Nigeria, July to September 2014.

We analyse up-to-date epidemiological data of the Ebola virus disease outbreak in Nigeria as of 1 October 2014 in order to estimate the case fatality rate, the proportion of healthcare workers infected and the transmission tree. We also model the impact of control interventions on the size of the epidemic. Results indicate that Nigeria's quick and forceful implementation of control interventions was determinant in controlling the outbreak rapidly and avoiding a far worse scenario in this country.

Impact of weekday social contact patterns on the modeling of influenza transmission, and determination of the influenza latent period.

Human social contact patterns show marked day-of-week variations, with a higher frequency of contacts occurring during weekdays when children are in school, and adults are in contact with co-workers, than typically occur on weekends. Using epidemic modeling, we show that using the average of social contacts during the week in the model yields virtually identical predictions of epidemic final size and the timing of the epidemic incidence peak as a model that incorporates weekday social contact patterns. This is true of models with a constant weekly average contact rate throughout the year, and also of models that assume seasonality of transmission. Our modeling studies reveal, however, that weekday social contact patterns can produce substantial weekday variations in an influenza incidence curve, and the pattern of variation is sensitive to the influenza latent period. The possible observability of weekday patterns in daily influenza incidence data opens up an interesting avenue of further inquiry that can shed light on the latent period of pandemic influenza. The duration of the latent period must be known with precision in order to design effective disease intervention strategies, such as use of antivirals. For a hypothetical influenza pandemic, we thus perform a simulation study to determine the number of cases needed to observe the weekday variation pattern in influenza epidemic incidence data. Our studies suggest that these patterns should be observable at 95% confidence in daily influenza hospitalization data from large cities over 75% of the time. Using 2009 A(H1N1) daily case data recorded by a large hospital in Santiago, Chile, we show that significant weekday incidence patterns are evident. From these weekday incidence patterns, we estimate the latent period of influenza to be [0.04, 0.60] days (95% CI). This method for determination of the influenza latent period in a community setting is novel, and unique in its approach.

Risk factors for mortality among 2009 A/H1N1 influenza hospitalizations in Maricopa County, Arizona, April 2009 to March 2010.

We analyzed individual-level data on pandemic influenza A/H1N1pdm hospitalizations from the enhanced surveillance system of the Maricopa County Department of Public Health, AZ, USA from April 1st, 2009 to March 31st, 2010. We also assessed the the risk of death among A/H1N1 hospitalizations using multivariate logistic regression. Hospitalization rates were significantly higher among Native Americans (risk ratio (RR)  =  6.2; 95% CI: 6.15, 6.21), non-Hispanic Black (RR = 3.84; 95% CI: 3.8, 3.9), and Hispanics (RR = 2.0; 95% CI: 2.0, 2.01) compared to non-Hispanic Whites. Throughout the spring, 59.2% of hospitalized patients received antiviral treatment; the proportion of patients treated increased significantly during the fall to 74.4% (Chi-square test, P < 0.0001). In our best-fit logistic model, the adjusted risk of death among A/H1N1 inpatients was significantly higher during the fall wave (August 16, 2009 to March 31, 2010, OR = 3.94; 95% CI: 1.72, 9.03) compared to the spring wave (April 1, 2009 to August 15, 2009). Moreover, chronic lung disease (OR = 3.5; 95% CI: 1.7, 7.4), cancer within the last 12 months (OR = 4.3; 95%CI: 1.3, 14.8), immuno-suppression (OR = 4.0; 95% CI: 1.84, 8.9), and admission delays (OR = 4.6; 95% CI: 2.2, 9.5) were significantly associated with an increased the risk of death among A/H1N1 inpatients.

The 1918-1920 influenza pandemic in Peru.

BACKGROUND: Increasing our knowledge of past influenza pandemic patterns in different regions of the world is crucial to guide preparedness plans against future influenza pandemics. Here, we undertook extensive archival collection efforts from three representative cities of Peru-Lima in the central coast, Iquitos in the northeastern Amazon region, Ica in the southern coast-to characterize the temporal, age and geographic patterns of the 1918-1920 influenza pandemic in this country. MATERIALS AND METHODS: We analyzed historical documents describing the 1918-1920 influenza pandemic in Peru and retrieved individual mortality records from local provincial archives for quantitative analysis. We applied seasonal excess mortality models to daily and monthly respiratory mortality rates for 1917-1920 and quantified transmissibility estimates based on the daily growth rate in respiratory deaths. RESULTS: A total of 52,739 individual mortality records were inspected from local provincial archives. We found evidence for an initial mild pandemic wave during July-September 1918 in Lima, identified a synchronized severe pandemic wave of respiratory mortality in all three locations during November 1918-February 1919, and a severe pandemic wave during January 1920-March 1920 in Lima and July-October 1920 in Ica. There was no recrudescent pandemic wave in 1920 in Iquitos. Remarkably, Lima experienced the brunt of the 1918-1920 excess mortality impact during the 1920 recrudescent wave, with all age groups experiencing an increase in all cause excess mortality from 1918-1919 to 1920. Middle age groups experienced the highest excess mortality impact, relative to baseline levels, in the 1918-1919 and 1920 pandemic waves. Cumulative excess mortality rates for the 1918-1920 pandemic period were higher in Iquitos (2.9%) than Lima (1.6%). The mean reproduction number for Lima was estimated in the range 1.3-1.5. CONCLUSIONS: We identified synchronized pandemic waves of intense excess respiratory mortality during November 1918-February 1919 in Lima, Iquitos, Ica, followed by asynchronous recrudescent waves in 1920. Cumulative data from quantitative studies of the 1918 influenza pandemic in Latin American settings have confirmed the high mortality impact associated with this pandemic. Further historical studies in lesser studied regions of Latin America, Africa, and Asia are warranted for a full understanding of the global impact of the 1918 pandemic virus.

Rubella metapopulation dynamics and importance of spatial coupling to the risk of congenital rubella syndrome in Peru.

Rubella is generally a mild childhood disease, but infection during early pregnancy may cause spontaneous abortion or congenital rubella syndrome (CRS), which may entail a variety of birth defects. Consequently, understanding the age-structured dynamics of this infection has considerable public health value. Vaccination short of the threshold for local elimination of transmission will increase the average age of infection. Accordingly, the classic concern for this infection is the potential for vaccination to increase incidence in individuals of childbearing age. A neglected aspect of rubella dynamics is how age incidence patterns may be moulded by the spatial dynamics inherent to epidemic metapopulations. Here, we use a uniquely detailed dataset from Peru to explore the implications of this for the burden of CRS. Our results show that the risk of CRS may be particularly severe in small remote regions, a prediction at odds with expectations in the endemic situation, and with implications for the outcome of vaccination. This outcome results directly from the metapopulation context: specifically, extinction-re-colonization dynamics are crucial because they allow for significant leakage of susceptible individuals into the older age classes during inter-epidemic periods with the potential to increase CRS risk by as much as fivefold.

Lessons from Nigeria: the role of roads in the geo-temporal progression of avian influenza (H5N1) virus.

The daily progression of the 2006 (January-June) Nigerian avian influenza (AI H5N1) epidemic was assessed in relation to both spatial variables and the generation interval of the invading virus. Proximity to the highway network appeared to promote epidemic dispersal: from the first AI generation interval onwards > 20% of all cases were located at < 5 km from the nearest major road. Fifty-seven per cent of all cases were located 31 km from three highway intersections. Findings suggest that the spatial features of emerging infections could be key in their control. When the spatial location of a transmission factor is well known, such as that of the highway network, and a substantial percentage of cases (e.g. > 20%) are near that factor, early interventions focusing on transmission factors, such as road blocks that prevent poultry trade, may be more efficacious than interventions applied only to the susceptible population.

Pandemic influenza in a southern hemisphere setting: the experience in Peru from May to September, 2009.

This paper presents a description of Peru s experience with pandemic H1N1 influenza 2009. It is based on data from four main surveillance systems: a) ongoing sentinel surveillance of influenza-like illness cases with virological surveillance of influenza and other respiratory viruses; b) sentinel surveillance of severe acute respiratory infections and associated deaths; c) surveillance of acute respiratory infections in children under the age of five years and pneumonia in all age groups; and d) case and cluster surveillance. On 9 May 2009, the first confirmed case of pandemic H1N1 influenza in Peru was diagnosed in a Peruvian citizen returning from New York with a respiratory illness. By July, community transmission of influenza had been identified and until 27 September 2009, a total of 8,381 cases were confirmed. The incidence rate per 10,000 persons was 4.4 (in the 0-9 year-olds) and 4.1 (in the 10-19 year-olds). During epidemiological weeks (EW) 26 to 37, a total of 143 fatal cases were notified (a case fatality of 1.71%, based on confirmed cases). The maximum peak in the number of cases was reached in EW 30 with 37 deaths. Currently, the impact of the pandemic in the Peruvian population has not been too severe, and fortunately, healthcare centres have not been overwhelmed. However, the future of this pandemic is uncertain and despite the fact that our country has not been seriously affected, we should be prepared for upcoming pandemic waves.

Qualitative analysis of the level of cross-protection between epidemic waves of the 1918-1919 influenza pandemic.

The 1918-1919 influenza pandemic was composed of multiple waves within a period of nine months in several regions of the world. Increasing our understanding of the mechanisms responsible for this multi-wave profile has important public health implications. We model the transmission dynamics of two strains of influenza interacting via cross-immunity to simulate two temporal waves of influenza and explore the impact of the basic reproduction number, as a measure of transmissibility associated to each influenza strain, cross-immunity and the timing of the onset of the second influenza epidemic on the pandemic profile. We use time series of case notifications during the 1918 influenza pandemic in Geneva, Switzerland, for illustration. We calibrate our mathematical model to the initial wave of infection to estimate the basic reproduction number of the first wave and the corresponding timing of onset of the second influenza variant. We use this information to explore the impact of cross-immunity levels on the dynamics of the second wave of influenza. Our results for the 1918 pandemic in Geneva, Switzerland, indicate that a second wave can occur whenever R 01 < 1.5 or when cross-immunity levels are less than 0.58 for our estimated R 02 of 2.4. We also explore qualitatively profiles of two-wave pandemics and compare them with real temporal profiles of the 1918 influenza pandemic in other regions of the world including several Scandinavian cities, New York City, England and Wales, and Sydney, Australia. Pandemic profiles are classified into three broad categories namely "right-handed", "left-handed", and "M-shape". Our results indicate that avoiding a second influenza epidemic is plausible given sufficient levels of cross-protection are attained via natural infection during an early (herald) wave of infection or vaccination campaigns prior to a second wave. Furthermore, interventions aimed at mitigating the first pandemic wave may be counterproductive by increasing the chances of a second wave of infection that could potentially be more virulent than the first.