Preventing Zika virus infection during pregnancy by timing conception seasonally Preventing Zika Virus Infection during Pregnancy by Timing Conception Seasonally Transmission Seasonality

It has come to light that Zika virus (ZIKV) infection during pregnancy can result in transplacental transmission to the fetus along with fetal death, congenital microcephaly and/or Central Nervous System (CNS) malformations. There are projected to be > 9, 200, 000 births annually in countries with ongoing ZIKV transmission. In response to the ZIKV threat, options to the full extent of the law" 88 Aedes aegypti has seasonal variation in its ability to facilitate flavivirus transmission 89 because its abundance and competence as a vector are affected by temperature and 90 rainfal

Data from: Opportunities and challenges of Integral Projection Models for modeling host-parasite dynamics

Epidemiological dynamics are shaped by and may in turn shape host demography. These feedbacks can result in hard to predict patterns of disease incidence. Mathematical models that integrate infection and demography are consequently a key tool for informing expectations for disease burden and identifying effective measures for control. A major challenge is capturing the details of infection within individuals and quantifying their downstream impacts to understand population-scale outcomes. For example, parasite loads and antibody titres may vary over the course of an infection and contribute to differences in transmission at the scale of the population. To date, to capture these subtleties, models have mostly relied on complex mechanistic frameworks, discrete categorization and/or agent-based approaches. Integral Projection Models (IPMs) allow variance in individual trajectories of quantitative traits and their population-level outcomes to be captured in ways that directly reflect statistical models of trait–fate relationships. Given increasing data availability, and advances in modelling, there is considerable potential for extending this framework to traits of relevance for infectious disease dynamics. Here, we provide an overview of host and parasite natural history contexts where IPMs could strengthen inference of population dynamics, with examples of host species ranging from mice to sheep to humans, and parasites ranging from viruses to worms. We discuss models of both parasite and host traits, provide two case studies and conclude by reviewing potential for both ecological and evolutionary research

Data from: Combined genetic and telemetry data reveal high rates of gene flow, migration, and long-distance dispersal potential in Arctic ringed seals (Pusa hispida)

Ringed seals (Pusa hispida) are broadly distributed in seasonally ice covered seas, and their survival and reproductive success is intricately linked to sea ice and snow. Climatic warming is diminishing Arctic snow and sea ice and threatens to endanger ringed seals in the foreseeable future. We investigated the population structure and connectedness within and among three subspecies: Arctic (P. hispida hispida), Baltic (P. hispida botnica), and Lake Saimaa (P. hispida saimensis) ringed seals to assess their capacity to respond to rapid environmental changes. We consider (a) the geographical scale of migration, (b) use of sea ice, and (c) the amount of gene flow between subspecies. Seasonal movements and use of sea ice were determined for 27 seals tracked via satellite telemetry. Additionally, population genetic analyses were conducted using 354 seals representative of each subspecies and 11 breeding sites. Genetic analyses included sequences from two mitochondrial regions and genotypes of 9 microsatellite loci. We found that ringed seals disperse on a pan-Arctic scale and both males and females may migrate long distances during the summer months when sea ice extent is minimal. Gene flow among Arctic breeding sites and between the Arctic and the Baltic Sea subspecies was high; these two subspecies are interconnected as are breeding sites within the Arctic subspecies

Data from: Combined genetic and telemetry data reveal high rates of gene flow, migration, and long-distance dispersal potential in Arctic ringed seals (Pusa hispida)

Ringed seals (Pusa hispida) are broadly distributed in seasonally ice covered seas, and their survival and reproductive success is intricately linked to sea ice and snow. Climatic warming is diminishing Arctic snow and sea ice and threatens to endanger ringed seals in the foreseeable future. We investigated the population structure and connectedness within and among three subspecies: Arctic (P. hispida hispida), Baltic (P. hispida botnica), and Lake Saimaa (P. hispida saimensis) ringed seals to assess their capacity to respond to rapid environmental changes. We consider (a) the geographical scale of migration, (b) use of sea ice, and (c) the amount of gene flow between subspecies. Seasonal movements and use of sea ice were determined for 27 seals tracked via satellite telemetry. Additionally, population genetic analyses were conducted using 354 seals representative of each subspecies and 11 breeding sites. Genetic analyses included sequences from two mitochondrial regions and genotypes of 9 microsatellite loci. We found that ringed seals disperse on a pan-Arctic scale and both males and females may migrate long distances during the summer months when sea ice extent is minimal. Gene flow among Arctic breeding sites and between the Arctic and the Baltic Sea subspecies was high; these two subspecies are interconnected as are breeding sites within the Arctic subspecies

Additional file 1 of Identifying optimal vaccination scenarios to reduce varicella zoster virus transmission and reactivation

Additional file 1: Supplementary Materials

Pusa hispida mtDNA Control Region (bps 90-565)

Sequences from the mtDNA control region (CR). 99 individuals were sequenced. The ReadMe file contains the breeding site (i.e., sample site) of each individual. Uncertainty in sequence accuracy occurred at the ends of each sequence; therefore, we used a 476 bp subset (bps 90–565) of CR. A map of the sample sites can be found in Figure 2A of our publication ``Combined Genetic and Telemetry Data Reveal High Rates of Gene Flow, Migration, and Long-Distance Dispersal Potential in Arctic Ringed Seals (Pusa hispida)''

Pusa hispida mtDNA COI (bps 92-450)

Sequences of the mtDNA Cytochrome oxidase I region (COI) from 113 individuals from 8 breeding sites. Uncertainty in sequence accuracy occurred at the ends of each sequence; thus, we used a 359 base pair (bp) subset (bps 92-450) of the COI region. The breeding site (i.e., sample site) associated with each sequence is in the ReadMe file. A map of the sample sites can be found in Figure 2A of our publication ``Combined Genetic and Telemetry Data Reveal High Rates of Gene Flow, Migration, and Long-Distance Dispersal Potential in Arctic Ringed Seals (Pusa hispida)''

Pusa hispida Microsatellite Genotypes

Microsatellite genotypes in the data input format of the program GenAlEx. The data are codominant genotypic microsatellite data, with loci scored as fragment size. There are 9 microsatellite loci, and 354 individuals sampled from 11 breeding sites. For a map showing the geographic locations of the breeding sites (i.e., sample sites) see Figure 2A in our publication ``Combined Genetic and Telemetry Data Reveal High Rates of Gene Flow, Migration, and Long-Distance Dispersal Potential in Arctic Ringed Seals (Pusa hispida)''

Panmixia and genetic differentiation between subspecies and breeding populations of ringed seals.

<p>Breeding sites from left-to-right: Kotzebue, Peard Bay, Paktoa, Tuktoyaktuk, Ulukhaktok/Holman, Baltic Sea, and Lake Saimaa. Populations with the same color and connected by a line were deemed panmictic based on pairwise permutation tests using (A) mtDNA Cytochrome Oxidase I, (B) mtDNA control region, and (C) microsatellites. Non-panmictic sites are significantly differentiated from other sites (p-values <0.05). Breeding sites left-to-right in panel C: Kotzebue, Peard Bay, Barrow, Oliktok, Prudhoe, Kaktovik, Paktoa, Tuktoyaktuk, Ulukhaktok/Holman, Baltic Sea, and Lake Saimaa.</p