Practical considerations for measuring the effective reproductive number,Rt

Estimation of the effective reproductive number R_t is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using R_t to assess the effectiveness of interventions and to inform policy. However, estimation of R_t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R_t, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting R_t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R_t estimation.     

Dendra2 Photoswitching through the Mammary Imaging Window

In the last decade, intravital microscopy of breast tumors in mice and rats at single-cell resolution^1-4 has resulted in important insights into mechanisms of metastatic behavior such as migration, invasion and intravasation of tumor cells^{5, 6}, angiogenesis³ and immune cells response^7-9. We have recently reported a technique to image orthotopic mammary carcinomas over multiple intravital imaging sessions in living mice¹⁰. For this, we have developed a Mammary Imaging Window (MIW) and optimized imaging parameters for Dendra2¹¹ photoswitching and imaging in vivo. Here, we describe the protocol for the manufacturing of MIW, insertion of the MIW on top of a tumor and imaging of the Dendra2- labeled tumor cells using a custom built imaging box. This protocol can be used to image the metastatic behavior of tumor cells in distinct microenvironments in tumors and allows for long term imaging of blood vessels, tumor cells and host cells.Open in a separate windowClick here to view.^{(56M, flv)}     

Social contacts and mixing patterns relevant to the spread of infectious diseases

Background

Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology.

Methods and Findings

7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible.

Conclusions

To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.     

A Highlights from MBoC Selection: PTP1B-dependent regulation of receptor tyrosine kinase signaling by the actin-binding protein Mena

During breast cancer progression, alternative mRNA splicing produces functionally distinct isoforms of Mena, an actin regulator with roles in cell migration and metastasis. Aggressive tumor cell subpopulations express Mena^INV, which promotes tumor cell invasion by potentiating EGF responses. However, the mechanism by which this occurs is unknown. Here we report that Mena associates constitutively with the tyrosine phosphatase PTP1B and mediates a novel negative feedback mechanism that attenuates receptor tyrosine kinase signaling. On EGF stimulation, complexes containing Mena and PTP1B are recruited to the EGFR, causing receptor dephosphorylation and leading to decreased motility responses. Mena also interacts with the 5′ inositol phosphatase SHIP2, which is important for the recruitment of the Mena-PTP1B complex to the EGFR. When Mena^INV is expressed, PTP1B recruitment to the EGFR is impaired, providing a mechanism for growth factor sensitization to EGF, as well as HGF and IGF, and increased resistance to EGFR and Met inhibitors in signaling and motility assays. In sum, we demonstrate that Mena plays an important role in regulating growth factor–induced signaling. Disruption of this attenuation by Mena^INV sensitizes tumor cells to low–growth factor concentrations, thereby increasing the migration and invasion responses that contribute to aggressive, malignant cell phenotypes.     

Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.     

Transmission of Novel Influenza A(H1N1) in Households with Post-Exposure Antiviral Prophylaxis

Background

Despite impressive advances in our understanding of the biology of novel influenza A(H1N1) virus, little is as yet known about its transmission efficiency in close contact places such as households, schools, and workplaces. These are widely believed to be key in supporting propagating spread, and it is therefore of importance to assess the transmission levels of the virus in such settings.

Methodology/Principal Findings

We estimate the transmissibility of novel influenza A(H1N1) in 47 households in the Netherlands using stochastic epidemic models. All households contained a laboratory confirmed index case, and antiviral drugs (oseltamivir) were given to both the index case and other households members within 24 hours after detection of the index case. Among the 109 household contacts there were 9 secondary infections in 7 households. The overall estimated secondary attack rate is low (0.075, 95%CI: 0.037–0.13). There is statistical evidence indicating that older persons are less susceptible to infection than younger persons (relative susceptibility of older persons: 0.11, 95%CI: 0.024–0.43. Notably, the secondary attack rate from an older to a younger person is 0.35 (95%CI: 0.14–0.61) when using an age classification of ≤12 versus >12 years, and 0.28 (95%CI: 0.12–0.50) when using an age classification of ≤18 versus >18 years.

Conclusions/Significance

Our results indicate that the overall household transmission levels of novel influenza A(H1N1) in antiviral-treated households were low in the early stage of the epidemic. The relatively high rate of adult-to-child transmission indicates that control measures focused on this transmission route will be most effective in minimizing the total number of infections.     

Collagen-based cell migration models in vitro and in vivo

Fibrillar collagen is the most abundant extracellular matrix (ECM) constituent which maintains the structure of most interstitial tissues and organs, including skin, gut, and breast. Density and spatial alignments of the three-dimensional (3D) collagen architecture define mechanical tissue properties, i.e. stiffness and porosity, which guide or oppose cell migration and positioning in different contexts, such as morphogenesis, regeneration, immune response, and cancer progression. To reproduce interstitial cell movement in vitro with high in vivo fidelity, 3D collagen lattices are being reconstituted from extracted collagen monomers, resulting in the re-assembly of a fibrillar meshwork of defined porosity and stiffness. With a focus on tumor invasion studies, we here evaluate different in vitro collagen-based cell invasion models, employing either pepsinized or non-pepsinized collagen extracts, and compare their structure to connective tissue in vivo, including mouse dermis and mammary gland, chick chorioallantoic membrane (CAM), and human dermis. Using confocal reflection and two-photon-excited second harmonic generation (SHG) microscopy, we here show that, depending on the collagen source, in vitro models yield homogeneous fibrillar texture with a quite narrow range of pore size variation, whereas all in vivo scaffolds comprise a range from low- to high-density fibrillar networks and heterogeneous pore sizes within the same tissue. Future in-depth comparison of structure and physical properties between 3D ECM-based models in vitro and in vivo are mandatory to better understand the mechanisms and limits of interstitial cell movements in distinct tissue environments.