Person:

Santillana, Mauricio

We present a computationally efficient adaptive method for calculating the time evolution of the concentrations of chemical species in global 3-D models of atmospheric chemistry. Our strategy consists of partitioning the computational domain into fast and slow regions for each chemical species at every time step. In each grid box, we group the fast species and solve for their concentration in a coupled fashion. Concentrations of the slow species are calculated using a simple semi-implicit formula. Separation of species between fast and slow is done on the fly based on their local production and loss rates. This allows for example to exclude short-lived volatile organic compounds (VOCs) and their oxidation products from chemical calculations in the remote troposphere where their concentrations are negligible, letting the simulation determine the exclusion domain and allowing species to drop out individually from the coupled chemical calculation as their production/loss rates decline. We applied our method to a 1-year simulation of global tropospheric ozone-NOx-VOC-aerosol chemistry using the GEOS-Chem model. Results show a 50% improvement in computational performance for the chemical solver, with no significant added error.

Dengue is a common and growing problem worldwide, with an estimated 70–140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models.

Objective: To evaluate the association between Dengue Fever (DF) and climate in Mexico with real-time data from Google Dengue Trends (GDT) and climate data from NASA Earth observing systems. Introduction: The incidence of dengue fever (DF) has increased 30 fold between 1960 and 2010 [1]. The literature suggests that temperature plays a major role in the life cycle of the mosquito vector and in turn, the timing of DF outbreaks [2]. We use real-time data from GDT and real-time temperature estimates from NASA Earth observing systems to examine the relationship between dengue and climate in 17 Mexican states from 2003–2011. For the majority of states, we predict that a warming climate will increase the number of days the minimum temperature is within the risk range for dengue. Methods: The GDT estimates are derived from internet search queries and use similar methods as those developed for Google Flu Trends [3]. To validate GDT data, we ran a correlation between GDT and dengue data from the Mexican Secretariat of Health (2003–2010). To analyze the relationship between GDT and varying lags of temperature, we constructed a time series meta-analysis. The mean, max and min of temperature were tested at lags 0 –12 weeks using data from the Modern Era Retrospective-Analysis for Research and Applications. Finally, we built a binomial model to identify the minimum 5° C temperature range associated with a 50% or higher Dengue activity threshold as predicted by GDT. Results: The time series plot of GDT data and the Mexican Secretariat of Health data (2003– 2010) (Figure 1) produced a correlation coefficient of 0.87. The time series meta-analysis results for 17 states showed an increase in minimum temperature at lag week 8 had the greatest odds of dengue incidence, 1.12 Odds Ratio (1.09–1.16, 95% Confidence Interval). The comparison of dengue activity above 50% in each state to the minimum temperature at lag week 8 showed 14/17 states had an association with warmest 5 degrees of the minimum temperature range. The state of Sonora was the only state to show an association between dengue and the coldest 5 degrees of the minimum temperature range. Conclusions: Overall, the incidence data from the Mexican Secretariat of Health showed a close correlation with the GDT data. The meta-analysis indicates that an increase in the minimum temperature at lag week 8 is associated with an increased dengue risk. This is consistent with the Colon-Gonzales et al. Mexico study which also found a strong association with the 8 week lag of increasing minimum temperature [4]. The results from this binomial regression show, for the majority of states, the warmest 5 degree range for the minimum temperature had the greatest association with dengue activity 8 weeks later. Inevitably, several other factors contribute to dengue risk which we are unable to include in this model [5]. IPCC climate change predictions suggest a 4° C increase in Mexico. Under such scenario, we predict an increase in the number of days the minimum temperature falls within the range associated with DF risk.

Background: Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015–2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission. Methodology/Principal Findings We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015–2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions. Significance Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak.

Background: Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report. Objective: Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY’s high-participation users, and summarize results from a user survey of a cohort of FNY participants. Methods: We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY’s 2015 and 2016 end-of-season user surveys were calculated. Results: During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor’s degree or higher. Conclusions: The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data.

Background: Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention’s (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users’ search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC’s flu reports. Methods: We extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013–2016 using multiple metrics including root mean squared error (RMSE). Results: Our method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons. Conclusions: Our method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu. Electronic supplementary material The online version of this article (doi:10.1186/s12879-017-2424-7) contains supplementary material, which is available to authorized users.

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

The United States (US) has been among those nations most severely affected by the first—and subsequent—phases of the pandemic of COVID-19, the disease caused by SARS-CoV-2. With only 4% of the worldwide population, the US has seen about 22% of COVID-19 deaths. Despite formidable advantages in resources and expertise, presently the per capita mortality rate is over 585/million, respectively 2.4 and 5 times higher compared to Canada and Germany. As we enter Fall 2020, the US is enduring ongoing outbreaks across large regions of the country. Moreover, within the US, an early and persistent feature of the pandemic has been the disproportionate impact on populations already made vulnerable by racism and dangerous jobs, inadequate wages, and unaffordable housing, and this is true for both the headline public health threat and the additional disastrous economic impacts. In this article we assess the impact of missteps by the Federal Government in three specific areas: the introduction of the virus to the US and the establishment of community transmission; the lack of national COVID-19 workplace standards and enforcement, and lack of personal protective equipment (PPE) for workplaces as represented by complaints to the Occupational Safety and Health Administration (OSHA) which we find are correlated with deaths 16 days later (ρ = 0.83); and the total excess deaths in 2020 to date already total more than 230,000, while COVID-19 mortality rates exhibit severe—and rising—inequities in race/ethnicity, including among working age adults.

The current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality –measured either by direct COVID-19 attributed deaths or excess deaths– and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the week to week burden of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the available positive COVID-19 case counts. We introduce three complementary approaches aimed at estimating the prevalence of COVID-19 in each state in the US as well as in New York City. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 case counts usually vary from 10 to 100 times greater than the official positive test counts. Nationally, our lowest and highest estimates of COVID-19 cases in the US from March 1, 2020 to April 4, 2020 are 2.7 and 8.3 million (9 to 27 times greater). These estimates are to be compared to the cumulative confirmed cases of about 311,000 as of April 4th. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 in other countries with comparable influenza surveillance systems.