Emerging Challenges and Opportunities in Infectious Disease Epidemiology.

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners

Short-term leprosy forecasting from an expert opinion survey.

We conducted an expert survey of leprosy (Hansen's Disease) and neglected tropical disease experts in February 2016. Experts were asked to forecast the next year of reported cases for the world, for the top three countries, and for selected states and territories of India. A total of 103 respondents answered at least one forecasting question. We elicited lower and upper confidence bounds. Comparing these results to regression and exponential smoothing, we found no evidence that any forecasting method outperformed the others. We found evidence that experts who believed it was more likely to achieve global interruption of transmission goals and disability reduction goals had higher error scores for India and Indonesia, but lower for Brazil. Even for a disease whose epidemiology changes on a slow time scale, forecasting exercises such as we conducted are simple and practical. We believe they can be used on a routine basis in public health

COVID-19: Forecasting confirmed cases and deaths with a simple time-series model

Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant

Economic Uncertainties in Valuing Reductions in Children's Environmental Health Risks

The recognition that environmental hazards can affect children differently and more severely than adults has provoked growing concern in industrialized nations about the impact of environmental pollution on children’s health. In this paper, commissioned by the OECD, we are charged with examining “economic uncertainties” associated with valuing the benefits of environmental policies that reduce risk to children’s health. We examine two sources of uncertainty in benefits estimation: forecasting uncertainty and modeling uncertainty. We explore how these sources of uncertainty affect the use of standard economic and non-economic approaches to the valuation of health benefits. These include willingness-to-pay measures, cost-of-illness and human-capital measures, and quality-adjusted life years (QALYs) and related non-economic measures.willingness to pay, QALY, children, social welfare function, health valuation, environmental health, household behavior

Demographic Forecasting: 1980 to 2005 in Review

Approaches and developments in demographic and population forecasting since 1980 are reviewed. Three approaches to forecasting demographic processes are extrapolation, expectation (individual-level birth expectations or population-level opinions of experts), and theory-based structural modelling involving exogenous variables. Models include 0-3 factors (age, period and cohort). Decomposition and disaggregation are also used in multistate models, including macrosimulation and microsimulation. Forecasting demographic change is difficult; accuracy depends on the particular situation or trends, but it is not clear when a method will perform best. Estimates of uncertainty (model-based ex ante error, expert-opinion-based ex ante error, and ex post error) differ; uncertainty estimation is highly uncertain. Probabilistic population forecasts are based on stochastic population renewal or random scenarios. The approaches to population forecasting, demographic process forecasting and error estimation are closely linked. Complementary methods that combine approaches are increasingly employed. The paper summarises developments, assesses progress and considers the future

How well did experts and laypeople forecast the size of the COVID-19 pandemic?

Throughout the COVID-19 pandemic, social and traditional media have disseminated predictions from experts and nonexperts about its expected magnitude. How accurate were the predictions of 'experts'-individuals holding occupations or roles in subject-relevant fields, such as epidemiologists and statisticians-compared with those of the public? We conducted a survey in April 2020 of 140 UK experts and 2,086 UK laypersons; all were asked to make four quantitative predictions about the impact of COVID-19 by 31 Dec 2020. In addition to soliciting point estimates, we asked participants for lower and higher bounds of a range that they felt had a 75% chance of containing the true answer. Experts exhibited greater accuracy and calibration than laypersons, even when restricting the comparison to a subset of laypersons who scored in the top quartile on a numeracy test. Even so, experts substantially underestimated the ultimate extent of the pandemic, and the mean number of predictions for which the expert intervals contained the actual outcome was only 1.8 (out of 4), suggesting that experts should consider broadening the range of scenarios they consider plausible. Predictions of the public were even more inaccurate and poorly calibrated, suggesting that an important role remains for expert predictions as long as experts acknowledge their uncertainty

How well did experts and laypeople forecast the size of the COVID-19 pandemic?

Throughout the COVID-19 pandemic, social and traditional media have disseminated predictions from experts and nonexperts about its expected magnitude. How accurate were the predictions of 'experts'-individuals holding occupations or roles in subject-relevant fields, such as epidemiologists and statisticians-compared with those of the public? We conducted a survey in April 2020 of 140 UK experts and 2,086 UK laypersons; all were asked to make four quantitative predictions about the impact of COVID-19 by 31 Dec 2020. In addition to soliciting point estimates, we asked participants for lower and higher bounds of a range that they felt had a 75% chance of containing the true answer. Experts exhibited greater accuracy and calibration than laypersons, even when restricting the comparison to a subset of laypersons who scored in the top quartile on a numeracy test. Even so, experts substantially underestimated the ultimate extent of the pandemic, and the mean number of predictions for which the expert intervals contained the actual outcome was only 1.8 (out of 4), suggesting that experts should consider broadening the range of scenarios they consider plausible. Predictions of the public were even more inaccurate and poorly calibrated, suggesting that an important role remains for expert predictions as long as experts acknowledge their uncertainty

Comparing human and model-based forecasts of COVID-19 in Germany and Poland

Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways

Summary results of the 2014-2015 DARPA Chikungunya challenge

BACKGROUND: Emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting. METHODS: To explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014–2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners. RESULTS: Participant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy. CONCLUSION: We conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics

Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures

Aggregated human judgment forecasts for COVID-19 targets of public health importance are accurate, often outperforming computational models. Our work shows aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as tool to aid public health decision making during outbreaks