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Climate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R 0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.

Accurate forecasts of influenza incidence can be used to inform medical and public health decision-making and response efforts. However, forecasting systems are uncommon in most countries, with a few notable exceptions. Here we use publicly available data from the World Health Organization to generate retrospective forecasts of influenza peak timing and peak intensity for 64 countries, including 18 tropical and subtropical countries. We find that accurate and well-calibrated forecasts can be generated for countries in temperate regions, with peak timing and intensity accuracy exceeding 50% at four and two weeks prior to the predicted epidemic peak, respectively. Forecasts are significantly less accurate in the tropics and subtropics for both peak timing and intensity. This work indicates that, in temperate regions around the world, forecasts can be generated with sufficient lead time to prepare for upcoming outbreak peak incidence.

Pathogen-pathogen interactions represent a critical but little-understood feature of infectious disease dynamics. In particular, experimental evidence suggests that influenza virus and respiratory syncytial virus (RSV) compete with each other, such that infection with one confers temporary protection against the other. However, such interactions are challenging to study using common epidemiologic methods. Here, we use a mathematical modeling approach, in conjunction with detailed surveillance data from Hong Kong and Canada, to infer the strength and duration of the interaction between influenza and RSV. Based on our estimates, we further utilize our model to evaluate the potential conflicting effects of live attenuated influenza vaccines (LAIV) on RSV burden. We find evidence of a moderate to strong, negative, bidirectional interaction, such that infection with either virus yields 40-100% protection against infection with the other for one to five months. Assuming that LAIV reduces RSV susceptibility in a similar manner, we predict that the impact of such a vaccine at the population level would likely depend greatly on underlying viral circulation patterns. More broadly, we highlight the utility of mathematical models as a tool to characterize pathogen-pathogen interactions. Influenza viruses and respiratory syncytial viruses may interfere with one another. Here, authors fit mathematical models of virus transmission, and find evidence of a bidirectional, moderate to strong, long-lasting interaction effect.

Past work has shown that models incorporating human travel can improve the quality of influenza forecasts. Here, we develop and validate a metapopulation model of twelve European countries, in which international translocation of virus is driven by observed commuting and air travel flows, and use this model to generate influenza forecasts in conjunction with incidence data from the World Health Organization. We find that, although the metapopulation model fits the data well, it offers no improvement over isolated models in forecast quality. We discuss several potential reasons for these results. In particular, we note the need for data that are more comparable from country to country, and offer suggestions as to how surveillance systems might be improved to achieve this goal.

Despite the availability of effective vaccines, the persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggests that cocirculation with other pathogens and resulting multiepidemics (of, for example, COVID-19 and influenza) may become increasingly frequent. To better forecast and control the risk of such multiepidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. Our review is structured in four parts. To study pathogen interactions in a systematic and comprehensive way, we first developed a general framework to capture their major components: sign (either negative for antagonistic interactions or positive for synergistic interactions), strength (i.e., magnitude of the interaction), symmetry (describing whether the interaction depends on the order of infection of interacting pathogens), duration (describing whether the interaction is short-lived or long-lived), and mechanism (e.g., whether interaction modifies susceptibility to infection, transmissibility of infection, or severity of disease). Second, we reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of coinfection with nonattenuated influenza A viruses (IAVs), and 3 with other pathogens. The 11 studies on IAV used different designs and animal models (ferrets, hamsters, and mice) but generally demonstrated that coinfection increased disease severity compared with either monoinfection. By contrast, the effect of coinfection on the viral load of either virus was variable and inconsistent across studies. Third, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only a few were specifically designed to infer interaction, and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with a reduced risk of SARS-CoV-2 infection. Finally, fourth, we formulated simple transmission models of SARS-CoV-2 cocirculation with an epidemic viral pathogen or an endemic bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.

Although forecasts and other mathematical models have the potential to play an important role in mitigating the impact of infectious disease outbreaks, the extent to which these tools are used in public health decision making in the United States is unclear. Throughout 2015, we invited public health practitioners belonging to three national public health organizations to complete a cross-sectional survey containing questions on model awareness, model use, and communication with modelers. Of 39 respondents, 46.15% used models in their work, and 20.51% reported direct communication with those who create models. Over half (64.10%) were aware that influenza forecasts exist. The need for improved communication between practitioners and modelers was overwhelmingly endorsed, with over 50% of participants indicating the need for models more relevant to public health questions, increased frequency of telecommunication, and more plain language in discussing models. Model use for public health decision making must be improved if models are to reach their full potential as public health tools. Increased quality and frequency of communication between practitioners and modelers could be particularly useful in achieving this goal. It is important that improvements be made now, rather than waiting for the next public health crisis to occur.

Although pneumococcal conjugate vaccines (PCVs) have greatly reduced diseases caused by vaccine-targeted serotypes (VT) of Streptococcus pneumoniae , vaccine impact may be eroded by the increase in rates of disease caused by non-vaccine serotypes (NVT). Here, we investigated the effect of social contact patterns on the dynamics of vaccine impact in carriage. We developed a neutral, age-structured, Susceptible–Colonized model incorporating VT-NVT co-colonization and verified it against real-world carriage data in children. Using social contact matrices from 34 countries, we assessed the impact of contact patterns on the time required to eliminate VT in a given age group (defined here as the time needed for the relative proportion of VT in overall carriage to drop by 95% after the introduction of vaccination). Finally, we quantified the contribution of various parameters—such as vaccine efficacy, coverage, immunity waning, and population susceptibility—to the dynamics of VT elimination. Our model recapitulated the observed decline of VT carriage and showed that varying the contact structure alone led to different time-to-elimination (3.8–6 years). We found that higher total contact rate and assortativity in children under 5 accelerated VT elimination. Additionally, higher vaccine efficacy and coverage, and slower immunity waning led to shorter time-to-elimination. These findings illuminate the mechanisms controlling the dynamics of vaccine impact and may help predict the impact of PCVs in communities with different contact patterns.

Background Socioeconomic disparities in COVID-19 burden were widely observed during the pandemic’s early waves, including in Germany, but studies on whether these inequalities have persisted or changed as the pandemic progressed are lacking. Methods We used an ecological study design to assess the relationship between a range of demographic, socioeconomic, and healthcare-related predictors and COVID-19 impact in Germany. Specifically, we fit generalized additive models to cumulative, district-level ( n  = 400) COVID-19 incidence and case fatality rates (CFRs) for each of the first five pandemic waves, which covered the period from March 2020 through May 2022. Results We find that associations between socioeconomic deprivation and COVID-19 impact evolved over time. Specifically, districts with higher levels of deprivation experienced lower incidence initially, but higher incidence beginning in the second half of wave 1 and persisting through wave 3. Meanwhile, more deprived districts experienced higher CFRs initially as well as during waves 3 through 5, but lower CFRs during the second half of wave 1. Finally, during the first four waves, we find that district-level CFRs scaled superlinearly with incidence, suggesting that the risk of death increased with incidence. This relationship was particularly strong during the first pandemic wave. Conclusions The association between socioeconomic position and COVID-19 impact in Germany has been complex, with patterns changing in intensity and direction over time. Continued monitoring of socioeconomic inequalities in COVID-19 impact, in particular at the individual level, is needed to better understand if and how inequalities continue to persist. Such monitoring will be instrumental in informing more equitable control strategies.

The combination of irradiated fibroblast feeder cells and Rho kinase inhibitor, Y-27632, conditionally induces an indefinite proliferative state in primary mammalian epithelial cells. These conditionally reprogrammed cells (CRCs) are karyotype-stable and nontumorigenic. Because self-renewal is a recognized property of stem cells, we investigated whether Y-27632 and feeder cells induced a stem-like phenotype. We found that CRCs share characteristics of adult stem cells and exhibit up-regulated expression of α6 and β1 integrins, ΔNp63α, CD44, and telomerase reverse transcriptase, as well as decreased Notch signaling and an increased level of nuclear β-catenin. The induction of CRCs is rapid (occurs within 2 d) and results from reprogramming of the entire cell population rather than the selection of a minor subpopulation. CRCs do not overexpress the transcription factor sets characteristic of embryonic or induced pluripotent stem cells (e.g., Sox2, Oct4, Nanog, or Klf4). The induction of CRCs is also reversible, and removal of Y-27632 and feeders allows the cells to differentiate normally. Thus, when CRCs from ectocervical epithelium or tracheal epithelium are placed in an air–liquid interface culture system, the cervical cells form a well differentiated stratified squamous epithelium, whereas the tracheal cells form a ciliated airway epithelium. We discuss the diagnostic and therapeutic opportunities afforded by a method that can generate adult stem-like cells in vitro without genetic manipulation.

Background Practising unprotected anal intercourse (UAI) with high numbers of partners is associated with increased risk for acquiring and transmitting HIV and other sexually transmitted infections. Our aim was to describe factors associated with UAI with multiple partners in a large sample of MSM from 38 European countries recruited for an online survey in 2010. Methods Data are from the European Men-Who-Have-Sex-With-Men Internet Survey (EMIS). The analysis was restricted to men who reported any anal sex with a non-steady partner in the past 12 months, and who were either never diagnosed with HIV, or who had been diagnosed with HIV more than 12 months ago, reported a detectable viral load and did not exclusively serosort ( n  = 91,477). Multivariable logistic regression was used to compare men reporting UAI with four or more (4+) non-steady partners to two comparison groups: a) no UAI with non-steady partners, and b) UAI with 1-3 non-steady partners. Results Overall, 9.6 % of the study population reported UAI with 4+ partners in the past 12 months. In both models, factors consistently associated with this behaviour were: having been diagnosed with HIV, lower educational levels, use of nitrite inhalants, drugs associated with sex and parties, or erectile dysfunction drugs in the past 4 weeks, using sex-on-site venues in the past 4 weeks, buying or selling sex in the past 12 months, having experienced physical violence due to sexual attraction to men in the past 12 months, reporting sexual happiness, being out to all or almost all of one’s acquaintances, and knowing that ART reduces HIV transmissibility. Conclusions Effective antiretroviral treatment drastically reduces HIV transmission for men diagnosed with HIV, irrespective of partner numbers. Apart from reducing partner numbers or increasing condom use no other recommendations are currently in place to reduce the risk of HIV acquisition and onward transmission for HIV-negative men practicing UAI with multiple partners. A range of factors were identified as associated with UAI with four or more partners which allow the strengthening and targeting of prevention strategies to reduce HIV transmission risks resulting from condomless anal intercourse with multiple partners.