Autochthonous Plasmodium vivax Infections, Florida, USA, 2023.

During May-July 2023, a cluster of 7 patients at local hospitals in Florida, USA, received a diagnosis of Plasmodium vivax malaria. Whole-genome sequencing of the organism from 4 patients and phylogenetic analysis with worldwide representative P. vivax genomes indicated probable single parasite introduction from Central/South America.

Assessing the risk of malaria local transmission and re-introduction in China from pre-elimination to elimination: A systematic review.

Assessing the risk of malaria local transmission and re-introduction is crucial for the preparation and implementation of an effective elimination campaign and the prevention of malaria re-introduction in China. Therefore, this review aims to evaluate the risk factors for malaria local transmission and re-introduction in China over the period of pre-elimination to elimination. Data were obtained from six databases searched for studies that assessed malaria local transmission risk before malaria elimination and re-introduction risk after the achievement of malaria elimination in China since the launch of the NMEP in 2010, employing the keywords "malaria" AND ("transmission" OR "re-introduction") and their synonyms. A total of 8,124 articles were screened and 53 articles describing 55 malaria risk assessment models in China from 2010 to 2023, including 40 models assessing malaria local transmission risk (72.7%) and 15 models assessing malaria re-introduction risk (27.3%). Factors incorporated in the 55 models were extracted and classified into six categories, including environmental and meteorological factors (39/55, 70.9%), historical epidemiology (35/55, 63.6%), vectorial factors (32/55, 58.2%), socio-demographic information (15/26, 53.8%), factors related to surveillance and response capacity (18/55, 32.7%), and population migration aspects (13/55, 23.6%). Environmental and meteorological factors as well as vectorial factors were most commonly incorporated in models assessing malaria local transmission risk (29/40, 72.5% and 21/40, 52.5%) and re-introduction risk (10/15, 66.7% and 11/15, 73.3%). Factors related to surveillance and response capacity and population migration were also important in malaria re-introduction risk models (9/15, 60%, and 6/15, 40.0%). A total of 18 models (18/55, 32.7%) reported the modeling performance. Only six models were validated internally and five models were validated externally. Of 53 incorporated studies, 45 studies had a quality assessment score of seven and above. Environmental and meteorological factors as well as vectorial factors play a significant role in malaria local transmission and re-introduction risk assessment. The factors related to surveillance and response capacity and population migration are more important in assessing malaria re-introduction risk. The internal and external validation of the existing models needs to be strengthened in future studies.

Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States.

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.

A discrete-time survival model for porcine epidemic diarrhoea virus.

Since the arrival of porcine epidemic diarrhea virus (PEDV) in the United States in 2013, elimination and control programmes have had partial success. The dynamics of its spread are hard to quantify, though previous work has shown that local transmission and the transfer of pigs within production systems are most associated with the spread of PEDV. Our work relies on the history of PEDV infections in a region of the southeastern United States. This infection data is complemented by farm-level features and extensive industry data on the movement of both pigs and vehicles. We implement a discrete-time survival model and evaluate different approaches to modelling the local-transmission and network effects. We find strong evidence in that the local-transmission and pig-movement effects are associated with the spread of PEDV, even while controlling for seasonality, farm-level features and the possible spread of disease by vehicles. Our fully Bayesian model permits full uncertainty quantification of these effects. Our farm-level out-of-sample predictions have a receiver-operating characteristic area under the curve (AUC) of 0.779 and a precision-recall AUC of 0.097. The quantification of these effects in a comprehensive model allows stakeholders to make more informed decisions about disease prevention efforts.

Importation, Local Transmission, and Model Selection in Estimating the Transmissibility of COVID-19: The Outbreak in Shaanxi Province of China as a Case Study.

Background: Since the emergence of the COVID-19 pandemic, many models have been applied to understand its epidemiological characteristics. However, the ways in which outbreak data were used in some models are problematic, for example, importation was mixed up with local transmission. Methods: In this study, five models were proposed for the early Shaanxi outbreak in China. We demonstrated how to select a reasonable model and correctly use the outbreak data. Bayesian inference was used to obtain parameter estimates. Results: Model comparison showed that the renewal equation model generates the best model fitting and the Susceptible-Exposed-Diseased-Asymptomatic-Recovered (SEDAR) model is the worst; the performance of the SEEDAR model, which divides the exposure into two stages and includes the pre-symptomatic transmission, and SEEDDAAR model, which further divides infectious classes into two equally, lies in between. The Richards growth model is invalidated by its continuously increasing prediction. By separating continuous importation from local transmission, the basic reproduction number of COVID-19 in Shaanxi province ranges from 0.45 to 0.61, well below the unit, implying that timely interventions greatly limited contact between people and effectively contained the spread of COVID-19 in Shaanxi. Conclusions: The renewal equation model provides the best modelling; mixing continuous importation with local transmission significantly increases the estimate of transmissibility.

Prevention of indigenous malaria cases by strengthening migration surveillance at village level in Purbalingga Regency, Central Java Province, Indonesia.

Purbalingga Regency had achieved zero indigenous malaria cases in April 2016, 3 years before the targeted deadline for elimination. Currently, the biggest threat to elimination efforts is the risk of local malaria reintroduction due to imported cases in the receptive areas. The aim of this study was to describe the implementation of village level migration surveillance and identify the areas for improvement in it. We performed the study in four malaria-free focus villages in Purbalingga Regency, i.e., Pengadegan, Sidareja, Panusupan, and Rembang, from March to October 2019. A total of 108 participants were involved in the processes. Data on malaria vector species, community mobility from malaria-endemic areas, and implementation of malaria migration surveillance (MMS) were collected. We use descriptive analysis for quantitative data and thematic content for qualitative data. Socialization of migration surveillance in Pengadegan and Sidareja villages has been carried out to the wider community, whereas in Panusupan and Tunjungmuli villages, it is still limited to neighbors. Communities in Pengadegan and Sidareja villages have participated in reporting the arrivals of migrant workers, and the village malaria interpreter conducts blood tests on all migrants. Community participation in reporting migrant workers arriving in Panusupan and Tunjungmuli villages is still low. Recording of migrant data reporting has been carried out by MMS officers, but malaria checks are only carried out before Eid al-Fitr to prevent importation of malaria cases. The program needs to strengthen the community mobilization and case-finding efforts.

Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales.

Inferring the transmission potential of an infectious disease during low-incidence periods following epidemic waves is crucial for preparedness. In such periods, scarce data may hinder existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether elevating caseloads (requiring swift community-wide interventions) or local elimination (allowing controls to be relaxed or refocussed on case-importation) might occur can separate decisive from ineffective policy. By generalizing and fusing recent approaches, we propose a novel early-warning framework that maximizes the information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any scale of investigation (assuming sufficiently good surveillance). Applying this framework, we decipher hidden disease-transmission signals in prolonged low-incidence COVID-19 data from New Zealand, Hong Kong and Victoria, Australia. We uncover how timely interventions associate with averting resurgent waves, support official elimination declarations and evidence the effectiveness of the rapid, adaptive COVID-19 responses employed in these regions.

Use of hemagglutinin and neuraminidase amplicon-based high-throughput sequencing with variant analysis to detect co-infection and resolve identical consensus sequences of seasonal influenza in a university setting.

BACKGROUND: Local transmission of seasonal influenza viruses (IVs) can be difficult to resolve. Here, we study if coupling high-throughput sequencing (HTS) of hemagglutinin (HA) and neuraminidase (NA) genes with variant analysis can resolve strains from local transmission that have identical consensus genome. We analyzed 24 samples collected over four days in January 2020 at a large university in the US. We amplified complete hemagglutinin (HA) and neuraminidase (NA) genomic segments followed by Illumina sequencing. We identified consensus complete HA and NA segments using BLASTn and performed variant analysis on strains whose HA and NA segments were 100% similar. RESULTS: Twelve of the 24 samples were PCR positive, and we detected complete HA and/or NA segments by de novo assembly in 83.33% (10/12) of them. Similarity and phylogenetic analysis showed that 70% (7/10) of the strains were distinct while the remaining 30% had identical consensus sequences. These three samples also had IAV and IBV co-infection. However, subsequent variant analysis showed that they had distinct variant profiles. While the IAV HA of one sample had no variant, another had a T663C mutation and another had both C1379T and C1589A. CONCLUSION: In this study, we showed that HTS coupled with variant analysis of only HA and NA genes can help resolve variants that are closely related. We also provide evidence that during a short time period in the 2019-2020 season, co-infection of IAV and IBV occurred on the university campus and both 2020/2021 and 2021/2022 WHO recommended H1N1 vaccine strains were co-circulating.

Elucidating the Local Transmission Dynamics of Highly Pathogenic Avian Influenza H5N6 in the Republic of Korea by Integrating Phylogenetic Information.

Highly pathogenic avian influenza (HPAI) virus is one of the most virulent and infectious pathogens of poultry. As a response to HPAI epidemics, veterinary authorities implement preemptive depopulation as a controlling strategy. However, mass culling within a uniform radius of the infection site can result in unnecessary depopulation. Therefore, it is useful to quantify the transmission distance from infected premises (IPs) before determining the optimal area for preemptive depopulation. Accordingly, we analyzed the transmission risk within spatiotemporal clusters of IPs using transmission kernel estimates derived from phylogenetic clustering information on 311 HPAI H5N6 IPs identified during the 2016-2017 epidemic, Republic of Korea. Subsequently, we explored the impact of varying the culling radius on the local transmission of HPAI given the transmission risk estimates. The domestic duck farm density was positively associated with higher transmissibility. Ring culling over a radius of 3 km may be effective for areas with high dense duck holdings, but this approach does not appear to significantly reduce the risk for local transmission in areas with chicken farms. This study provides the first estimation of the local transmission dynamics of HPAI in the Republic of Korea as well as insight into determining an effective ring culling radius.

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach.

BACKGROUND: Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally. OBJECTIVE: South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19. METHODS: The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers. RESULTS: The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R0 was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The Rt was estimated in South Korea and in both Seoul and Gyeonggi. CONCLUSIONS: A statistical model accounting for imported and locally transmitted cases was employed to estimate R0 and Rt. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.

Validation of molecular clock inferred HIV infection ages: Evidence for accurate estimation of infection dates.

BACKGROUND: Improving HIV diagnosis, access to care and effective antiretroviral treatment provides our global strategy to reduce HIV incidence. To reach this goal we need to increase our knowledge about local epidemics. HIV infection dates would be an important information towards this goal, but they are largely unknown. To date, methods to estimate the dates of HIV infection are based mainly on laboratory or molecular methods. Our aim was to validate molecular clock inferred infection dates that were estimated by analysing sequences from 145 people living with HIV (PLHIV) with known transmission dates (clinically estimated infection dates). METHODS: All HIV sequences were obtained by Sanger sequencing and were previously found to belong to well-established molecular transmission clusters (MTCs). RESULTS: Our analysis showed that the molecular clock inferred infection dates were correlated with the clinically estimated ones (Spearman's Correlation coefficient = 0.93, p < 0.001) and that there was an agreement between them (Lin's concordance correlation coefficient = 0.92, p < 0.001). For the 61.4% of cases the molecular clock inferred preceded the clinically estimated infection dates. The median difference between clinically and molecularly estimated dates of infection was of 0.18 (IQR: -0.21, 0.89) years. The lowest differences were identified in people who inject drugs of our study population. CONCLUSIONS: The estimated time to more recent common ancestor (tMRCA) of nodes within clusters provides a reliable approximation of HIV infections for PLHIV infected within MTCs. Next-generation sequencing data and molecular clock estimates based on heterochronous sequences provide, probably, more reliable methods for inferring infection dates. However, since these data are not available in most of the HIV clinical laboratories, our approach, under specific conditions, can provide a reliable estimation of HIV infection dates and can be used for HIV public health interventions.

An outbreak investigation of scrub typhus in Nepal: confirmation of local transmission.

BACKGROUND: Scrub typhus is a largely ignored tropical disease and a leading cause of undifferentiated febrile illness in the areas of tsutsugamushi triangle caused by Orientia tsutsugamushi. It is frequently diagnosed in South Asian countries, although clear epidemiological information is not available from Nepal. After the 2015 earthquake in Nepal, a sudden upsurge in scrub typhus cases was reported. The objective of this study was to investigate epidemiology of scrub typhus and its causative agents in humans, animals, and chigger mites to understand the ongoing transmission ecology. METHODS: Scrub typhus cases with confirmed diagnosis throughout the country were included in the analysis. Studies were concentrated in the Chitwan district, the site of a major outbreak in 2016. Additional nation-wide data from 2015 to 2017 available from the government database included to analyse the disease distribution by geographical mapping. RESULTS: From 2015 to 2017, 1239 scrub typhus cases were confirmed with the largest outbreak occurring in 2016 with 831 (67.1%) cases. The case fatality rate was 5.7% in 2015 which declined to 1.1% in 2017. A nationwide outbreak of scrub typhus was declared as the cases were detected in 52 out of the 75 districts of Nepal. Seasonal trend was observed with a peak during August and September. In addition to the human cases, the presence of O. tsutsugamushi was also confirmed in animals (rodents) and chigger mites (Leptotrombidium imphalum) from the outbreak areas of southern Nepal. CONCLUSION: The detection of O. tsutsugamushi in humans, animals, and chigger mites from outbreak locations and wide-spread reports of scrub typhus throughout the country consecutively for 3 years confirms the ongoing transmission of O. tsutsugamushi with a firmly established ecology in Nepal. The country's health system needs to be strengthened for systematic surveillance, early outbreak detection, and immediate actions including treatment and preventive measures.

A Nationwide Study about the Dispersal Patterns of the Predominant HIV-1 Subtypes A1 and B in Greece: Inference of the Molecular Transmission Clusters.

Our aim was to investigate the dispersal patterns and parameters associated with local molecular transmission clusters (MTCs) of subtypes A1 and B in Greece (predominant HIV-1 subtypes). The analysis focused on 1751 (28.4%) and 2575 (41.8%) sequences of subtype A1 and B, respectively. Identification of MTCs was based on phylogenetic analysis. The analyses identified 38 MTCs including 2-1518 subtype A1 sequences and 168 MTCs in the range of 2-218 subtype B sequences. The proportion of sequences within MTCs was 93.8% (1642/1751) and 77.0% (1982/2575) for subtype A1 and B, respectively. Transmissions within MTCs for subtype A1 were associated with risk group (Men having Sex with Men vs. heterosexuals, OR = 5.34, p < 0.001) and Greek origin (Greek vs. non-Greek origin, OR = 6.05, p < 0.001) and for subtype B, they were associated with Greek origin (Greek vs. non-Greek origin, OR = 1.57, p = 0.019), younger age (OR = 0.96, p < 0.001), and more recent sampling (time period: 2011-2015 vs. 1999-2005, OR = 3.83, p < 0.001). Our findings about the patterns of across and within country dispersal as well as the parameters associated with transmission within MTCs provide a framework for the application of the study of molecular clusters for HIV prevention.

COVID-19: the current situation in Nepal.

The recent global pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly alarming. As of 21 June 2020, there are more than 8.7 million cases worldwide, with 460 000 deaths. Nepal is not an exception to COVID-19 and is currently facing a challenge to prevent the spread of infection. The analysis of the detected cases, severity and outcomes of the cases within a country is important to have a clear picture of where the pandemic is heading and what measures should be taken to curb the infection before it becomes uncontrollable. We collected data regarding all the cases, recoveries and deaths attributed to COVID-19 in Nepal starting from the first case on 23 January to 21 June 2020. At present, COVID-19 has spread all over Nepal, with a rapid increase in the number of new cases and deaths, which is alarming in a low-income country with an inadequate healthcare system like Nepal. Although the government implemented early school closure and lockdown, the management to contain COVID-19 does not appear to be adequate. Understanding the current situation regarding COVID-19 in Nepal is important for providing a direction towards proper management of the disease.

Tracking the spread of COVID-19 in India via social networks in the early phase of the pandemic.

BACKGROUND: The coronavirus pandemic (COVID-19) has spread worldwide via international travel. This study traced its diffusion from the global to national level and identified a few superspreaders that played a central role in the transmission of this disease in India. DATA AND METHODS: We used the travel history of infected patients from 30 January to 6 April 6 2020 as the primary data source. A total of 1386 cases were assessed, of which 373 were international and 1013 were national contacts. The networks were generated in Gephi software (version 0.9.2). RESULTS: The maximum numbers of connections were established from Dubai (degree 144) and the UK (degree 64). Dubai's eigenvector centrality was the highest that made it the most influential node. The statistical metrics calculated from the data revealed that Dubai and the UK played a crucial role in spreading the disease in Indian states and were the primary sources of COVID-19 importations into India. Based on the modularity class, different clusters were shown to form across Indian states, which demonstrated the formation of a multi-layered social network structure. A significant increase in confirmed cases was reported in states like Tamil Nadu, Delhi and Andhra Pradesh during the first phase of the nationwide lockdown, which spanned from 25 March to 14 April 2020. This was primarily attributed to a gathering at the Delhi Religious Conference known as Tabliqui Jamaat. CONCLUSIONS: COVID-19 got induced into Indian states mainly due to International travels with the very first patient travelling from Wuhan, China. Subsequently, the contacts of positive cases were located, and a significant spread was identified in states like Gujarat, Rajasthan, Maharashtra, Kerala and Karnataka. The COVID-19's spread in phase one was traced using the travelling history of the patients, and it was found that most of the transmissions were local.

Genetic evidence for imported malaria and local transmission in Richard Toll, Senegal.

BACKGROUND: Malaria elimination efforts can be undermined by imported malaria infections. Imported infections are classified based on travel history. METHODS: A genetic strategy was applied to better understand the contribution of imported infections and to test for local transmission in the very low prevalence region of Richard Toll, Senegal. RESULTS: Genetic relatedness analysis, based upon molecular barcode genotyping data derived from diagnostic material, provided evidence for both imported infections and ongoing local transmission in Richard Toll. Evidence for imported malaria included finding that a large proportion of Richard Toll parasites were genetically related to parasites from Thiès, Senegal, a region of moderate transmission with extensive available genotyping data. Evidence for ongoing local transmission included finding parasites of identical genotype that persisted across multiple transmission seasons as well as enrichment of highly related infections within the households of non-travellers compared to travellers. CONCLUSIONS: These data indicate that, while a large number of infections may have been imported, there remains ongoing local malaria transmission in Richard Toll. These proof-of-concept findings underscore the value of genetic data to identify parasite relatedness and patterns of transmission to inform optimal intervention selection and placement.

Confirmation of the absence of local transmission and geographic assignment of imported falciparum malaria cases to China using microsatellite panel.

BACKGROUND: Current methods to classify local and imported malaria infections depend primarily on patient travel history, which can have limited accuracy. Genotyping has been investigated as a complementary approach to track the spread of malaria and identify the origin of imported infections. METHODS: An extended panel of 26 microsatellites (16 new microsatellites) for Plasmodium falciparum was evaluated in 602 imported infections from 26 sub-Saharan African countries to the Jiangsu Province of People's Republic of China. The potential of the 26 microsatellite markers to assign imported parasites to their geographic origin was assessed using a Bayesian method with Markov Chain Monte Carlo (MCMC) as implemented in the program Smoothed and Continuous Assignments (SCAT) with a modification to incorporate haploid genotype data. RESULTS: The newly designed microsatellites were polymorphic and are not in linkage disequilibrium with the existing microsatellites, supporting previous findings of high rate of recombination in sub-Saharan Africa. Consistent with epidemiology inferred from patients' travel history, no evidence for local transmission was found; nearly all genetically related infections were identified in people who travelled to the same country near the same time. The smoothing assignment method assigned imported cases to their likely geographic origin with an accuracy (Angola: 59%; Nigeria: 51%; Equatorial Guinea: 40%) higher than would be achieved at random, reaching statistical significance for Angola and Equatorial Guinea. CONCLUSIONS: Genotyping using an extended microsatellite panel is valuable for malaria case classification and programme evaluation in an elimination setting. A Bayesian method for assigning geographic origin of mammals based on genetic data was adapted for malaria and showed potential for identification of the origin of imported infections.

The temperature and regional climate effects on communitarian COVID-19 contagion in Mexico throughout phase 1.

Due to the close relationship between the incidence of infectious diseases by epidemics and environmental conditions, this research explores the temperature, evaporation, precipitation and regional climate effects on the local transmission of coronavirus SARS-CoV-2 inside 31 states and capital of Mexico since February 29 (national onset) to March 31, 2020. Statistical analysis was conducted to explore the association between the daily local COVID-19 confirmed positive cases (LCPC) and both climate characteristics and the daily weather reported by the regional meteorological stations. In this work, the local transmission ratio (LTR) was calculated with the regional LCPC divided by the number of the effective contagion days since regional onset in each state. The results showed a negative association between temperature (mean, max and min) and climate classification with both LCPC and LTR variables. The precipitation associated positively with LCPC and LTR. The associations between the climate classification with LCPC and LTR are statistically significant. The tropical climate (mean temperature around 25.95 °C and mean precipitation around 8.74 mm) delayed the regional onset. However, the regional onset in dry climates emerged earlier as consequence of the lower temperatures and higher precipitations (20.57 °C and 20.87 mm respectively) than the observed in the tropical climate. The fastest regional onsets were observed in tempered climates in states where the lowest temperatures and lowest precipitations were registered (19.65 °C and 8.48 mm respectively). Meteorological factors influenced the trend on the regional outbreaks in Mexican's states likely by the host predisposition and susceptibility during the cold winter season. In Mexico, the climate characteristics played a crucial role on the local infection during the phase 1 being the tempered regions (as Michoacán, Jalisco, Puebla, etc.) more vulnerable than the dry (as Chihuahua, Durango or Zacatecas, etc.) or tropical areas (as Colima, Campeche, Morelos etc.).