To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors. Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region. The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately.

The epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to preventLeptospirainfections.

Leptospirosis is a worldwide zoonotic bacterial disease caused by infection with leptospires. Leptospirosis in humans and livestock is an endemic and epidemic disease in Thailand. Livestock may act as reservoirs for leptospires and source for human infection. Data on leptospirosis infection in humans and livestock (Buffaloes, Cattle, and Pigs) species during 2010 to 2015 were analyzed. Serum samples were examined using Microscopic Agglutination Test (MAT) to identify antibodies against Leptospira serovars using a cut-off titer ≥ 1:100. The seroprevalence was 23.7% in humans, 24.8% in buffaloes, 28.1% in cattle, and 11.3% in pigs. Region specific prevalence among humans and livestock was found in a wide range. The most predominant serovars were Shermani, followed by Bratislava, Panama, and Sejroe in human, Shermani, Ranarum, and Tarassovi in buffaloes, and Shermani and Ranarum in cattle and pigs. Equally highest MAT titers against multiple serovars per one sample were found mainly in buffaloes and cattle showing equally titers against Ranarum and Shermani. The correlations of distribution of serovars across Thailand’s regions were found to be similar in pattern for cattle but not for buffaloes. In humans, the serovar distribution in the south differed from other regions. By logistic regression, the results indicated that livestock is more susceptible to infection by serovar Shermani when compared to humans. This study gives a detailed picture of the predominance of Leptospira serovars in relation to region, humans and typical livestock. The broad spatial distribution of seroprevalence was analyzed across and within species as well as regions in Thailand. Our finding may guide public health policy makers to implement appropriate control measures and help to reduce the impact of leptospirosis in Thailand. Leptospirosis is an important worldwide zoonotic disease, particularly in tropical and subtropical countries. The infection in humans is caused by either direct contact with products of infected animals, mainly urine, or by indirect contact via a contaminated environment. The animal hosts are thus considered reservoirs for human infection as livestock in Thailand usually live in close contact with householders in rural areas. However, the links of Leptospira serovars in humans and livestock in Thailand are poorly understood. Therefore, we illustrate the circulation of Leptospira serovars in humans and livestock during the past six years. The cross-correlations of the seroprevalence distribution were investigated to assess similarity between serovars across and within both, species and regions. The results suggest that livestock could be a potential source for human infection as sample analysis revealed a predominance of the same serovars. This information will increase public health awareness and may benefit especially high risk groups such as abattoir workers, livestock owners, farmers and other animal handling personal.

The emergence of drug resistance continuously threatens global control of infectious diseases, including malaria caused by the protozoan parasite Plasmodium falciparum. A critical parasite determinant is the P. falciparum chloroquine resistance transporter (PfCRT), the primary mediator of chloroquine (CQ) resistance (CQR), and a pleiotropic modulator of susceptibility to several first-line artemisinin-based combination therapy partner drugs. Aside from the validated CQR molecular marker K76T, P. falciparum parasites have acquired at least three additional pfcrt mutations, whose contributions to resistance and fitness have been heretofore unclear. Focusing on the quadruple-mutant Ecuadorian PfCRT haplotype Ecu1110 (K76T/A220S/N326D/I356L), we genetically modified the pfcrt locus of isogenic, asexual blood stage P. falciparum parasites using zinc-finger nucleases, producing all possible combinations of intermediate pfcrt alleles. Our analysis included the related quintuple-mutant PfCRT haplotype 7G8 (Ecu1110 + C72S) that is widespread throughout South America and the Western Pacific. Drug susceptibilities and in vitro growth profiles of our combinatorial pfcrt-modified parasites were used to simulate the mutational trajectories accessible to parasites as they evolved CQR. Our results uncover unique contributions to parasite drug resistance and growth for mutations beyond K76T and predict critical roles for the CQ metabolite monodesethyl-CQ and the related quinoline-type drug amodiaquine in driving mutant pfcrt evolution. Modeling outputs further highlight the influence of parasite proliferation rates alongside gains in drug resistance in dictating successful trajectories. Our findings suggest that P. falciparum parasites have navigated constrained pfcrt adaptive landscapes by means of probabilistically rare mutational bursts that led to the infrequent emergence of pfcrt alleles in the field.

Background: The Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.Survey Methodology: In this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms “dynamics,” “mathematical model,” “modeling,” and “vector-borne” together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were “compartmental,” “spatial,” “metapopulation,” “network,” “individual-based,” “agent-based” AND “Zika.” All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.Results: We found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.Discussion: Mathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.

Mathematical modeling has become a tool used to address many emerging diseases. One of the most basic and popular modeling frameworks is the compartmental model. Unfortunately, most of the available compartmental models developed for Zika virus (ZIKV) transmission were designed to describe and reconstruct only past, short-time ZIKV outbreaks in which the effects of seasonal change to entomological parameters can be ignored. To make an accurate long-term prediction of ZIKV transmission, the inclusion of seasonal effects into an epidemic model is unavoidable. We developed a vector-borne compartmental model to analyze the spread of the ZIKV during the 2015–2016 outbreaks in Bahia, Brazil and to investigate the impact of two vector control strategies, namely, reducing mosquito biting rates and reducing mosquito population size. The model considered the influences of seasonal change on the ZIKV transmission dynamics via the time-varying mosquito biting rate. The model was also validated by comparing the model prediction with reported data that were not used to calibrate the model. We found that the model can give a very good fit between the simulation results and the reported Zika cases in Bahia (R-square = 0.9989). At the end of 2016, the total number of ZIKV infected people was predicted to be 1.2087 million. The model also predicted that there would not be a large outbreak from May 2016 to December 2016 due to the decrease of the susceptible pool. Implementing disease mitigation by reducing the mosquito biting rates was found to be more effective than reducing the mosquito population size. Finally, the correlation between the time series of estimated mosquito biting rates and the average temperature was also suggested. The proposed ZIKV transmission model together with the estimated weekly biting rates can reconstruct the past long-time multi-peak ZIKV outbreaks in Bahia.