SignificanceForecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts.
Previous modeling studies have identified the vaccination coverage level necessary for preventing influenza epidemics, but have not shown whether this critical coverage can be reached. Here we use computational modeling to determine, for the first time, whether the critical coverage for influenza can be achieved by voluntary vaccination. We construct a novel individual-level model of human cognition and behavior; individuals are characterized by two biological attributes (memory and adaptability) that they use when making vaccination decisions. We couple this model with a population-level model of influenza that includes vaccination dynamics. The coupled models allow individual-level decisions to influence influenza epidemiology and, conversely, influenza epidemiology to influence individual-level decisions. By including the effects of adaptive decision-making within an epidemic model, we can reproduce two essential characteristics of influenza epidemiology: annual variation in epidemic severity and sporadic occurrence of severe epidemics. We suggest that individual-level adaptive decision-making may be an important (previously overlooked) causal factor in driving influenza epidemiology. We find that severe epidemics cannot be prevented unless vaccination programs offer incentives. Frequency of severe epidemics could be reduced if programs provide, as an incentive to be vaccinated, several years of free vaccines to individuals who pay for one year of vaccination. Magnitude of epidemic amelioration will be determined by the number of years of free vaccination, an individuals' adaptability in decision-making, and their memory. This type of incentive program could control epidemics if individuals are very adaptable and have long-term memories. However, incentive-based programs that provide free vaccination for families could increase the frequency of severe epidemics. We conclude that incentive-based vaccination programs are necessary to control influenza, but some may be detrimental. Surprisingly, we find that individuals' memories and flexibility in adaptive decision-making can be extremely important factors in determining the success of influenza vaccination programs. Finally, we discuss the implication of our results for controlling pandemics.
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We define and analyze an inductive reasoning game of voluntary yearly vaccination in order to establish whether or not a population of individuals acting in their own self-interest would be able to prevent influenza epidemics. We find that epidemics are rarely prevented. We also find that severe epidemics may occur without the introduction of pandemic strains. We further address the situation where market incentives are introduced to help ameliorating epidemics. Surprisingly, we find that vaccinating families exacerbates epidemics. However, a public health program requesting prepayment of vaccinations may significantly ameliorate influenza epidemics.Game theory has been very successful to help design economic market strategies. Recently, game theory has been applied in the field of theoretical epidemiology. Deductive reasoning games have been used to price vaccines [16] and to predict the voluntary vaccination coverage (i.e., the proportion of the population that gets vaccinated) for pathogens that provide permanent immunity (smallpox and measles) [5,4]. However, applying deductive reasoning may be limited [3] because it requires that individuals share the same perception of risk of infection and vaccination adverse effects. For pathogens that provide permanent immunity, using deductive reasoning games is still justified since individuals need to get vaccinated only once. However, in the case of pathogens that do not provide permanent immunity (e.g., influenza), individuals need to make vaccination decisions every year. It may be assumed that individuals make vaccination decisions based on their past experiences (i.e., use inductive reasoning) rather than based only on the current influenza epidemiology (i.e., use deductive reasoning). Here we present and analyze for the first time several inductive reasoning games that may apply to influenza vaccination.The first inductive reasoning model was introduced in 1994 with the El Farol bar problem [3]. We now briefly present this problem as a paradigm for inductive reasoning games. Consider a group of N individuals acting in their own self-interest. Each week, every individual independently decides whether or not to go to the El Farol bar. An individual deems it worth going if fewer than a critical fraction of the total number of individuals N are present. Otherwise, the bar is crowded and the individual would rather stay at home. No individual knows the bar attendance in advance, nor do they communicate with others; thus, the decision of going to the bar cannot be made in a logical deductive fashion. Instead, an individual would predict/guess the bar attendance and base their decisions on these predictions. Once the bar attendance is known, an individual evaluates and adapts their predictions for future decisions. This continuous adaptation of one's behavior based on expectations about future collective behavior is called inductive reasoning and applies in many instances where logical deductive reasoning fails either in principle or due to bounded rationality...
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