Coupling the Within-Host Process and Between-Host Transmission of COVID-19 Suggests Vaccination and School Closures are Critical.

Most models of COVID-19 are implemented at a single micro or macro scale, ignoring the interplay between immune response, viral dynamics, individual infectiousness and epidemiological contact networks. Here we develop a data-driven model linking the within-host viral dynamics to the between-host transmission dynamics on a multilayer contact network to investigate the potential factors driving transmission dynamics and to inform how school closures and antiviral treatment can influence the epidemic. Using multi-source data, we initially determine the viral dynamics and estimate the relationship between viral load and infectiousness. Then, we embed the viral dynamics model into a four-layer contact network and formulate an agent-based model to simulate between-host transmission. The results illustrate that the heterogeneity of immune response between children and adults and between vaccinated and unvaccinated infections can produce different transmission patterns. We find that school closures play a significant effect on mitigating the pandemic as more adults get vaccinated and the virus mutates. If enough infected individuals are diagnosed by testing before symptom onset and then treated quickly, the transmission can be effectively curbed. Our multiscale model reveals the critical role played by younger individuals and antiviral treatment with testing in controlling the epidemic.

Determining travel fluxes in epidemic areas.

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic.

Measles dynamics on network models with optimal control strategies.

To investigate the influences of heterogeneity and waning immunity on measles transmission, we formulate a network model with periodic transmission rate, and theoretically examine the threshold dynamics. We numerically find that the waning of immunity can lead to an increase in the basic reproduction number R 0 and the density of infected individuals. Moreover, there exists a critical level for average degree above which R 0 increases quicker in the scale-free network than in the random network. To design the effective control strategies for the subpopulations with different activities, we examine the optimal control problem of the heterogeneous model. Numerical studies suggest us no matter what the network is, we should implement control measures as soon as possible once the outbreak takes off, and particularly, the subpopulation with high connectivity should require high intensity of interventions. However, with delayed initiation of controls, relatively strong control measures should be given to groups with medium degrees. Furthermore, the allocation of costs (or resources) should coincide with their contact patterns.

Analysis of a multiscale HIV-1 model coupling within-host viral dynamics and between-host transmission dynamics.

There are many challenges to constitute the linkage from the macroscale to the microscale and analyze the multiscale model. We proposed a bidirectional coupling model with standard incidence which includes the interaction of between-host transmission dynamics and within-host viral dynamics, and investigated the dynamic behaviors of the multiscale system on two time-scales. We found that the multiscale system exhibits more complex dynamics including backward bifurcation, which means that the usual thresholds for infection control or virus elimination obtained from the epidemiological model or virus dynamic model may not act as threshold parameter under a certain condition. There may be multiple epidemic equilibriums, one of which is stable, although the basic reproduction number is less than 1. We numerically examine the synergistic impact between the macro and micro dynamics. In particular, increasing the drug efficacy can decrease the prevalence of disease. The contact rate may affect the number and size of equilibria of viral dynamics model by inducing the occurrence of backward bifurcation. The finding suggests that the effective control measures may include both the reduction in contact rate or transmission rate at the population level and the increase in drug efficacy at the individual level, and using these control measures together can effectively control the diseases.

Interface-induced superconductivity at ∼25 K at ambient pressure in undoped CaFe2As2 single crystals.

Superconductivity has been reversibly induced/suppressed in undoped CaFe2As2 (Ca122) single crystals through proper thermal treatments, with Tc at ∼25 K at ambient pressure and up to 30 K at 1.7 GPa. We found that Ca122 can be stabilized in two distinct tetragonal (T) phases at room temperature and ambient pressure: PI with a nonmagnetic collapsed tetragonal (cT) phase at low temperature and PII with an antiferromagnetic orthorhombic (O) phase at low temperature, depending on the low-temperature annealing condition. Neither phase at ambient pressure is superconducting down to 2 K. However, systematic annealing for different time periods at 350 °C on the as-synthesized crystals, which were obtained by quenching the crystal ingot from 850 °C, reveals the emergence of superconductivity over a narrow time window. Whereas the onset Tc is insensitive to the anneal time, the superconductive volume fraction evolves with the time in a dome-shaped fashion. Detailed X-ray diffraction profile analyses further reveal mesoscopically stacked layers of the PI and the PII phases. The deduced interface density correlates well with the superconducting volume measured. The transport anomalies of the T-cT transition, which is sensitive to lattice strain, and the T-O transition, which is associated with the spin-density-wave (SDW) transition, are gradually suppressed over the superconductive region, presumably due to the interface interactions between the nonmagnetic metallic cT phase and the antiferromagnetic O phase. The results provide the most direct evidence to date for interface-enhanced superconductivity in undoped Ca122, consistent with the recent theoretical prediction.

Synthesis, structure, and superconductivity in the new-structure-type compound: SrPt6P2.

A metal-rich ternary phosphide, SrPt(6)P(2), with a unique structure type was synthesized at high temperatures. Its crystal structure was determined by single-crystal X-ray diffraction [cubic space group Pa3̅; Z = 4; a = 8.474(2) Å, and V = 608.51(2) Å(3)]. The structure features a unique three-dimensional anionic (Pt(6)P(2))(2-) network of vertex-shared Pt(6)P trigonal prisms. The Sr atoms occupy a 12-coordinate (Pt) cage site and form a cubic close-packed (face-centered-cubic) arrangement, and the P atoms formally occupy tetrahedral interstices. The metallic compound becomes superconducting at 0.6 K, as evidenced by magnetic and resistivity measurements.

Unusual superconducting state at 49 K in electron-doped CaFe2As2 at ambient pressure.

We report the detection of unusual superconductivity up to 49 K in single crystalline CaFe(2)As(2) via electron-doping by partial replacement of Ca by rare-earth. The superconducting transition observed suggests the possible existence of two phases: one starting at 49 K, which has a low critical field < 4 Oe, and the other at 21 K, with a much higher critical field > 5 T. Our observations are in strong contrast to previous reports of doping or pressurizing layered compounds AeFe(2)As(2) (or Ae122), where Ae = Ca, Sr, or Ba. In Ae122, hole-doping has been previously observed to generate superconductivity with a transition temperature (T(c)) only up to 38 K and pressurization has been reported to produce superconductivity with a T(c) up to 30 K. The unusual 49 K phase detected will be discussed.

Superconducting Fe-based compounds (A1-xSrx)Fe2As2 with A=K and Cs with transition temperatures up to 37 K.

New high-T{c} Fe-based superconducting compounds, AFe2As2 with A=K, Cs, K/Sr, and Cs/Sr, were synthesized. The T{c} of KFe2As2 and CsFe2As2 is 3.8 and 2.6 K, respectively, which rises with partial substitution of Sr for K and Cs and peaks at 37 K for 50%-60% Sr substitution, and the compounds enter a spin-density-wave state with increasing electron number (Sr content). The compounds represent p-type analogs of the n-doped rare-earth oxypnictide superconductors. Their electronic and structural behavior demonstrate the crucial role of the (Fe2As2) layers in the superconductivity of the Fe-based layered systems, and the special feature of having elemental A layers provides new avenues to superconductivity at higher T{c}.