On the Dynamics of Dengue Virus type 2 with Residence Times and Vertical Transmission

A two-patch mathematical model of Dengue virus type 2 (DENV-2) that accounts for vectors' vertical transmission and between patches human dispersal is introduced. Dispersal is modeled via a Lagrangian approach. A host-patch residence-times basic reproduction number is derived and conditions under which the disease dies out or persists are established. Analytical and numerical results highlight the role of hosts' dispersal in mitigating or exacerbating disease dynamics. The framework is used to explore dengue dynamics using, as a starting point, the 2002 outbreak in the state of Colima, Mexico

Vertical Transmission in a Two-Strain Model of Dengue Fever

The role of vertical transmission in vectors has rarely been addressed in the study of dengue dynamics and control, in part because it was not considered a critical population-level factor. In this paper, we apply the pioneering model- ing ideas of Ross and MacDonald, motivated by the context of the 2000–2001 dengue outbreak in Peru, to assess the dynamics of multi-strain competition. An invading strain of dengue virus (DENV-2) from Asia rapidly circulated into Peru eventually displacing DENV-2 American. A host-dengue model that con- siders the competing dynamics of these two DENV-2 genotypes, the resident or the American type and the invasive more virulent Asian strain, is introduced and analyzed. The model incorporates vertical transmission by DENV-2 Asian a potentially advantageous trait. Conditions for competitive exclusion of dengue strains are established. The model is used to show that lower transmission rates of DENV-2 Asian are sufficient for displacing DENV-2 American in the presence of vertical transmission.National Institute of General Medical Sciences/[1R01GM100471-01]/NIH/Estados UnidosUCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de MatemáticaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Matemáticas Puras y Aplicadas (CIMPA

Illustration of Measurement Error Models for Reducing Bias in Nutrition and Obesity Research Using 2-D Body Composition Data

Objective: This study aimed to illustrate the use and value of measurement error models for reducing bias when evaluating associations between body fat and having type 2 diabetes (T2D) or being physically active. Methods: Logistic regression models were used to evaluate T2D and physical activity among adults aged 19 to 80 years from the Photobody Study (n = 558). Self‐reported T2D and physical activity were categorized as “yes” or “no.” Body fat measured by two‐dimensional photographs was adjusted for bias using dual‐energy x‐ray absorptiometry scans as a reference. Three approaches were applied: regression calibration (RC), simulation extrapolation (SIMEX), and multiple imputation (MI). Results: Unadjusted two‐dimensional measures of body fat had upward biases of 30% and 233% for physical activity and T2D, respectively. For the physical activity model, RC‐adjusted values had a 13% upward bias, whereas MI and SIMEX decreased the bias to 9% and 91%, respectively. For the T2D model, MI reduced the bias to 0%, whereas RC and SIMEX increased the upward bias to > 300%. Conclusions: Of three statistical approaches to reducing bias due to measurement errors, MI performed best in comparison to RC and SIMEX. Measurement error methods can improve the reliability of analyses that look for relations between body fat measures and health outcomes

Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models

The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions