Shu-Han You

Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: Insights from a statistical analysis

The purpose of this study was to link meteorological factors and mosquito (Aedes aegypti) abundance to examine the potential effects of climate variations on patterns of dengue epidemiology in Taiwan during 2001-2008. Spearmans rank correlation tests with and without time-lag were performed to investigate the overall correlation between dengue incidence rates and meteorological variables (i.e., minimum, mean, and maximum temperatures, relative humidity (RH), and rainfall) and percentage Breteau index (BI) level >2 in Taipei and Kaohsiung of northern and southern Taiwan, respectively. A Poisson regression analysis was performed by using a generalized estimating equations (GEE) approach. The most parsimonious model was selected based on the quasi-likelihood based information criterion (QICu). Spearmans rank correlation tests revealed marginally positive trends in the weekly mean (rho=0.28, p<0.0001), maximum (rho=0.26, p<0.0001), and minimum (rho=0.30, p<0.0001) temperatures in Taipei. However, in Kaohsiung, all negative trends were found in the weekly mean (rho=-0.32, p<0.0001), maximum (rho=-0.30, p<0.0001), and minimum (rho=-0.32, p<0.0001) temperatures. This study concluded that based on the GEE approach, rainfall, minimum temperature, and RH, all with 3-month lag, and 1-month lag of percentage BI level >2 are the significant predictors of dengue incidence in Kaohsiung (QICu=-277.77). This study suggested that warmer temperature with 3-month lag, elevated humidity with high mosquito density increased the transmission rate of human dengue fever infection in southern Taiwan.

A Probabilistic Transmission Model to Assess Infection Risk from Mycobacterium Tuberculosis in Commercial Passenger Trains

The objective of this article is to characterize the risk of infection from airborne Mycobacterium tuberculosis bacilli exposure in commercial passenger trains based on a risk-based probabilistic transmission modeling. We investigated the tuberculosis (TB) infection risks among commercial passengers by inhaled aerosol M. tuberculosis bacilli and quantify the patterns of TB transmission in Taiwan High Speed Rail (THSR). A deterministic Wells-Riley mathematical model was used to account for the probability of infection risk from M. tuberculosis bacilli by linking the cough-generated aerosol M. tuberculosis bacilli concentration and particle size distribution. We found that (i) the quantum generation rate of TB was estimated with a lognormal distribution of geometric mean (GM) of 54.29 and geometric standard deviation (GSD) of 3.05 quantum/h at particle size ≤ 5 μm and (ii) the basic reproduction numbers (R(0) ) were estimated to be 0.69 (0.06-6.79), 2.82 (0.32-20.97), and 2.31 (0.25-17.69) for business, standard, and nonreserved cabins, respectively. The results indicate that commercial passengers taking standard and nonreserved cabins had higher transmission risk than those in business cabins based on conservatism. Our results also reveal that even a brief exposure, as in the bronchoscopy cases, can also result in a transmission when the quantum generation rate is high. This study could contribute to a better understanding of the dynamics of TB transmission in commercial passenger trains by assessing the relationship between TB infectiousness, passenger mobility, and key model parameters such as seat occupancy, ventilation rate, and exposure duration.

Regional response of dengue fever epidemics to interannual variation and related climate variability

Dengue is a major international public health concern and one of the most important vector-borne diseases. The purpose of this article is to investigate the association among temperature, rainfall, relative humidity, and dengue fever by incorporating the lag effect and examining the dominant interannual model of the modern climate, the El Niño Southern Oscillation (ENSO), in the southern region of Taiwan. We built a linear Poisson regression model by including linear time treads and statistical indicators, verified with disease data in the 2004–2013 period. Here we showed that regional climatic factors in association with the interannual climate variability expressed by the ENSO phenomenon had a significant influence on the dynamics of urban dengue fever in southern Taiwan. The 2–4-month lag of statistical indicators of regional climate factors together with the 4-month lagged Pacific surface temperature (SST) anomaly in the proposed Poisson regression model could capture the regional dengue incidence patterns well. The statistical indicators of mean and coefficient of variation of temperature showed the greatest impact on the dengue incidence rate. We also found that the dengue incidence rate increased significantly with the lag effect of the warmer SST. The ability to forecast regional dengue incidence in southern Taiwan could permit pretreatment of mosquito habitats adjacent to human habitations with highly effective insecticides that would be released at the time of the high-temperature season.

Network information analysis reveals risk perception transmission in a behaviour-influenza dynamics system

Influenza poses a significant public health burden worldwide. Understanding how and to what extent people would change their behaviour in response to influenza outbreaks is critical for formulating public health policies. We incorporated the information-theoretic framework into a behaviour-influenza (BI) transmission dynamics system in order to understand the effects of individual behavioural change on influenza epidemics. We showed that information transmission of risk perception played a crucial role in the spread of health-seeking behaviour throughout influenza epidemics. Here a network BI model provides a new approach for understanding the risk perception spread and human behavioural change during disease outbreaks. Our study allows simultaneous consideration of epidemiological, psychological, and social factors as predictors of individual perception rates in behaviour-disease transmission systems. We suggest that a monitoring system with precise information on risk perception should be constructed to effectively promote health behaviours in preparation for emerging disease outbreaks.

Assessing risk perception and behavioral responses to influenza epidemics: linking information theory to probabilistic risk modeling

Risk perception plays a crucial role in shaping health-related behaviors in a variety of infectious disease control settings. The purpose of this study was to assess risk perception and behavioral changes in response to influenza epidemics. We present a risk perception assessment model that uses information theory linking with a probabilistic risk model to investigate the interplay between risk perception spread and health behavioral changes for an influenza outbreak. Building on human influenza data, we predicted risk perception spread as the amount of risk information. A negative feedback-based information model was used to explore whether health behavioral changes can increase the control effectiveness. Finally, a probabilistic risk assessment framework was used to predict influenza infection risk based on maximal information-derived risk perception. We found that (i) an individual who perceived more accurate knowledge of influenza can substantially increase the amount of mutual risk perception information, (ii) an intervening network over which individuals communicate overlap can be more effective in risk perception transfer, (iii) collective individual responses can increase risk perception information transferred, but may be limited by contact numbers of infectious individuals, and (iv) higher mutual risk perception information gains lower infection risk probability. We also revealed that when people increased information about the benefits of vaccination and antiviral drug used, future infections could significantly be prevented. We suggest that increasing mutual risk perception information through a negative feedback mechanism plays an important role in adaptation and mitigation behavior and policy support.

Assessing coughing-induced influenza droplet transmission and implications for infection risk control

Indoor transmission of respiratory droplets bearing influenza within humans poses high risks to respiratory function deterioration and death. Therefore, we aimed to develop a framework for quantifying the influenza infection risk based on the relationships between inhaled/exhaled respiratory droplets and airborne transmission dynamics in a ventilated airspace. An experiment was conducted to measure the size distribution of influenza-containing droplets produced by coughing for a better understanding of potential influenza spread. Here we integrated influenza population transmission dynamics, a human respiratory tract model, and a control measure approach to examine the indoor environment-virus-host interactions. A probabilistic risk model was implemented to assess size-specific infection risk for potentially transmissible influenza droplets indoors. Our results found that there was a 50% probability of the basic reproduction number (R0) exceeding 1 for small-size influenza droplets of 0·3-0·4 µm, implicating a potentially high indoor infection risk to humans. However, a combination of public health interventions with enhanced ventilation could substantially contain indoor influenza infection. Moreover, the present dynamic simulation and control measure assessment provide insights into why indoor transmissible influenza droplet-induced infection is occurring not only in upper lung regions but also in the lower respiratory tract, not normally considered at infection risk.

Using experimental human influenza infections to validate a viral dynamic model and the implications for prediction

The aim of this work was to use experimental infection data of human influenza to assess a simple viral dynamics model in epithelial cells and better understand the underlying complex factors governing the infection process. The developed study model expands on previous reports of a target cell-limited model with delayed virus production. Data from 10 published experimental infection studies of human influenza was used to validate the model. Our results elucidate, mechanistically, the associations between epithelial cells, human immune responses, and viral titres and were supported by the experimental infection data. We report that the maximum total number of free virions following infection is 10(3)-fold higher than the initial introduced titre. Our results indicated that the infection rates of unprotected epithelial cells probably play an important role in affecting viral dynamics. By simulating an advanced model of viral dynamics and applying it to experimental infection data of human influenza, we obtained important estimates of the infection rate. This work provides epidemiologically meaningful results, meriting further efforts to understand the causes and consequences of influenza A infection.

Mathematical modeling of postcoinfection with influenza A virus and Streptococcus pneumoniae , with implications for pneumonia and COPD-risk assessment

Background The interaction between influenza and pneumococcus is important for understanding how coinfection may exacerbate pneumonia. Secondary pneumococcal pneumonia associated with influenza infection is more likely to increase respiratory morbidity and mortality. This study aimed to assess exacerbated inflammatory effects posed by secondary pneumococcal pneumonia, given prior influenza infection. Materials and methods A well-derived mathematical within-host dynamic model of coinfection with influenza A virus and Streptococcus pneumoniae (SP) integrated with dose–response relationships composed of previously published mouse experimental data and clinical studies was implemented to study potentially exacerbated inflammatory responses in pneumonia based on a probabilistic approach. Results We found that TNFα is likely to be the most sensitive biomarker reflecting inflammatory response during coinfection among three explored cytokines. We showed that the worst inflammatory effects would occur at day 7 SP coinfection, with risk probability of 50% (likely) to develop severe inflammatory responses. Our model also showed that the day of secondary SP infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads. Conclusion People and health care workers should be wary of secondary SP infection on day 7 post-influenza infection for prompt and proper control-measure implementation. Our quantitative risk-assessment framework can provide new insights into improvements in respiratory health especially, predominantly due to chronic obstructive pulmonary disease (COPD).

Linking Contact Behavior and Droplet Patterns to Dynamically Model Indoor Respiratory Infections Among Schoolchildren

Background We used the results of a contact behavior survey in conjunction with droplet pattern measurement to investigate the indoor population transmission dynamics of respiratory infections. Methods A total of 404 questionnaires on all contact behaviors were distributed to junior high school students. Droplet number concentration and size distribution generated by coughing and talking were measured by droplet experimentation. A deterministic susceptible–exposed–infected–recovery (SEIR) model was used to simulate the indoor transmission dynamics of influenza infection among schoolchildren. Results Results indicated that the average contact rates ranged from 9.44 to 11.18 person−1 day−1 for grades 7 to 9. We showed that total median droplet number concentrations were 9.01 × 107 and 8.23 × 107 droplets per cubic meter for coughing and talking, respectively. Population dynamic simulations indicated that the size-dependent median number of droplets per person resulted in a maximum of 8 and 10 infected persons on day 4, respectively, for talking and coughing activities. Conclusions Human contact behavior and airborne droplet characteristics may substantially change predicted indoor population transmission dynamics of influenza infection.

Assessing health burden risk and control effect on dengue fever infection in the southern region of Taiwan

Background The high prevalence of dengue in Taiwan and the consecutive large dengue outbreaks in the period 2014–2015 suggest that current control interventions are suboptimal. Understanding the effect of control effort is crucial to inform future control strategies. Objectives We developed a framework to measure season-based health burden risk from 2001 to 2014. We reconstructed various intervention coverage to assess the attributable effect of dengue infection control efforts. Materials and methods A dengue–mosquito–human transmission dynamic was used to quantify the vector–host interactions and to estimate the disease epidemics. We used disability-adjusted life years (DALYs) to assess health burden risk. A temperature-basic reproduction number (R0)–DALYs relationship was constructed to examine the potential impacts of temperature on health burden. Finally, a health burden risk model linked a control measure model to evaluate the effect of dengue control interventions. Results We showed that R0 and DALYs peaked at 25°C with estimates of 2.37 and 1387, respectively. Results indicated that most dengue cases occurred in fall with estimated DALYs of 323 (267–379, 95% CI) at 50% risk probability. We found that repellent spray had by far the largest control effect with an effectiveness of ~71% in all seasons. Pesticide spray and container clean-up have both made important contributions to reducing prevalence/incidence. Repellent, pesticide spray, container clean-up together with Wolbachia infection suppress dengue outbreak by ~90%. Conclusion Our presented modeling framework provides a useful tool to measure dengue health burden risk and to quantify the effect of dengue control on dengue infection prevalence and disease incidence in the southern region of Taiwan.