Seasonality and the Coexistence of Pathogen Strains.

AbstractHost-pathogen models usually explain the coexistence of pathogen strains by invoking population structure, meaning host or pathogen variation across space or individuals; most models, however, neglect the seasonal variation typical of host-pathogen interactions in nature. To determine the extent to which seasonality can drive pathogen coexistence, we constructed a model in which seasonal host reproduction fuels annual epidemics, which are in turn followed by interepidemic periods with no transmission, a pattern seen in many host-pathogen interactions in nature. In our model, a pathogen strain with low infectiousness and high interepidemic survival can coexist with a strain with high infectiousness and low interepidemic survival: seasonality thus permits coexistence. This seemingly simple type of coexistence can be achieved through two very different pathogen strategies, but understanding these strategies requires novel mathematical analyses. Standard analyses show that coexistence can occur if the competing strains differ in terms of R0, the number of new infections per infectious life span in a completely susceptible population. A novel mathematical method of analyzing transient dynamics, however, allows us to show that coexistence can also occur if one strain has a lower R0 than its competitor but a higher initial fitness λ0, the number of new infections per unit time in a completely susceptible population. This second strategy allows coexisting pathogens to have quite similar phenotypes, whereas coexistence that depends on differences in R0 values requires that coexisting pathogens have very different phenotypes. Our novel analytic method suggests that transient dynamics are an overlooked force in host-pathogen interactions.

COVID-19 mortality attenuated during widespread Omicron transmission, Denmark, 2020 to 2022.

BackgroundIt sparked considerable attention from international media when Denmark lifted restrictions against COVID-19 in February 2022 amidst widespread transmission of the new SARS-CoV-2 Omicron variant and a steep rise in reported COVID-19 mortality based on the 30-day COVID-19 death count.AimOur aim was to investigate how coincidental infections affected COVID-19 mortality estimates following the introduction of the Omicron variant in late 2021.MethodsWe compared the 30-day COVID-19 death count with the observed mortality using three alternative mortality estimation methods; (i) a mathematical model to correct the 30-day COVID-19 death count for coincidental deaths, (ii) the Causes of Death Registry (CDR) and (iii) all-cause excess mortality.ResultsThere was a substantial peak in the 30-day COVID-19 death count following the emergence of the Omicron variant in late 2021. However, there was also a substantial change in the proportion of coincidental deaths, increasing from 10-20% to around 40% of the recorded COVID-19 deaths. The high number of 30-day COVID-19 deaths was not reflected in the number of COVID-19 deaths in the CDR and the all-cause excess mortality surveillance.ConclusionOur analysis showed a distinct change in the mortality pattern following the introduction of Omicron in late 2021 with a markedly higher proportion of people estimated to have died with, rather than of, COVID-19 compared with mortality patterns observed earlier in the COVID-19 pandemic. Our findings highlight the importance of incorporating alternative mortality surveillance methods to more correctly estimate the burden of COVID-19 as the pandemic continues to evolve.

Heterogeneity in testing for infectious diseases.

Testing strategies have varied widely between nation states during the COVID-19 pandemic, in intensity as well as methodology. Some countries have mainly performed diagnostic testing while others have opted for mass-screening for the presence of SARS-CoV-2 as well. COVID passport solutions have been introduced, in which access to several aspects of public life requires either testing, proof of vaccination or a combination thereof. This creates a coupling between personal activity levels and testing behaviour which, as we show in a mathematical model, leverages heterogeneous behaviours in a population and turns this heterogeneity from a disadvantage to an advantage for epidemic control.

Nationwide study on SARS-CoV-2 transmission within households from lockdown to reopening, Denmark, 27 February 2020 to 1 August 2020.

BackgroundThe COVID-19 pandemic is one of the most serious global public health threats of recent times. Understanding SARS-CoV-2 transmission is key for outbreak response and to take action against the spread of disease. Transmission within the household is a concern, especially because infection control is difficult to apply within this setting.AimThe objective of this observational study was to investigate SARS-CoV-2 transmission in Danish households during the early stages of the COVID-19 pandemic.MethodsWe used comprehensive administrative register data from Denmark, comprising the full population and all COVID-19 tests from 27 February 2020 to 1 August 2020, to estimate household transmission risk and attack rate.ResultsWe found that the day after receiving a positive test result within the household, 35% (788/2,226) of potential secondary cases were tested and 13% (98/779) of these were positive. In 6,782 households, we found that 82% (1,827/2,226) of potential secondary cases were tested within 14 days and 17% (371/2,226) tested positive as secondary cases, implying an attack rate of 17%. We found an approximate linear increasing relationship between age and attack rate. We investigated the transmission risk from primary cases by age, and found an increasing risk with age of primary cases for adults (aged ≥ 15 years), while the risk seems to decrease with age for children (aged < 15 years).ConclusionsAlthough there is an increasing attack rate and transmission risk of SARS-CoV-2 with age, children are also able to transmit SARS-CoV-2 within the household.

Loose Ends in the Epidemiology of the 1918 Pandemic: Explaining the Extreme Mortality Risk in Young Adults.

In the century since the 1918 influenza pandemic, insights have been sought to explain the pandemic's signature pattern of high death rates in young adults and low death rates in the elderly and infants. Our understanding of the origin and evolution of the pandemic has shifted considerably. We review evidence of the characteristic age-related pattern of death during the 1918 pandemic relative to the "original antigenic sin" hypothesis. We analyze age-stratified mortality data from Copenhagen around 1918 to identify break points associated with unusual death risk. Whereas infants had no meaningful risk elevation, death risk gradually increased, peaking for young adults 20-34 years of age before dropping sharply for adults ages 35-44 years, suggesting break points for birth cohorts around 1908 and 1878. Taken together with data from previous studies, there is strong evidence that those born before 1878 or after 1908 were not at increased risk of dying of 1918 pandemic influenza. Although the peak death risk coincided with the 1889-1892 pandemic, the 1908 and 1878 break points do not correspond with known pandemics. An increasing number of interdisciplinary studies covering fields such as virology, phylogenetics, death, and serology offer exciting insights into patterns and reasons for the unusual extreme 1918 pandemic mortality risk in young adults.

Epidemics in Competition: Partial Cross-Immunity.

The competition between two pathogen strains during the course of an epidemic represents a fundamental step in the early evolution of emerging diseases as well as in the antigenic drift process of influenza. The outcome of the competition, however, depends not only on the epidemic properties of the two strains but also on the timing and size of the introduction, characteristics that are poorly captured by deterministic mean-field epidemic models. We describe those aspects of the competition that can be determined from the mean-field models giving the range of possible final sizes of susceptible hosts and cumulated attack rates that could be observed after an epidemic with two cross-reacting strains. In the limit where the size of the initial infection goes to zero, the possible outcomes lie on a (one dimensional) curve in the outcome space.

A review of the 1918 herald pandemic wave: importance for contemporary pandemic response strategies.

Mounting epidemiological evidence supports the occurrence of a mild herald pandemic wave in the spring and summer of 1918 in North America and Europe, several months before the devastating autumn outbreak that killed an estimated 2% of the global population. These epidemiological findings corroborate the anecdotal observations of contemporary clinicians who reported widespread influenza outbreaks in spring and summer 1918, with sporadic occurrence of unusually severe clinical manifestations in young adults. Initially seen as controversial, these findings were eventually confirmed by retrospective identification of influenza specimens collected from U.S. soldiers who died from acute respiratory infections in May-August 1918. Other studies found that having an episode of influenza illness during the spring herald wave was highly protective in the severe autumn wave. Here, we conduct a systematic review of the clinical, epidemiological, and virological evidence supporting the global occurrence of mild herald waves of the 1918 pandemic and place these historic observations in the context of pandemic preparedness. Taken together, historic experience with the 1918 and subsequent pandemics shows that increased severity in second and later pandemic waves may be the rule rather than the exception. Thus, a sustained pandemic response in the first years following a future pandemic is critical; conversely, multiwave pandemic patterns allow for more time to rollout vaccines and antivirals.

Cholera Epidemics of the Past Offer New Insights Into an Old Enemy.

Background: Although cholera is considered the quintessential long-cycle waterborne disease, studies have emphasized the existence of short-cycle (food, household) transmission. We investigated singular Danish cholera epidemics (in 1853) to elucidate epidemiological parameters and modes of spread. Methods: Using time series data from cities with different water systems, we estimated the intrinsic transmissibility (R0). Accessing cause-specific mortality data, we studied clinical severity and age-specific impact. From physicians' narratives we established transmission chains and estimated serial intervals. Results: Epidemics were seeded by travelers from cholera-affected cities; initial transmission chains involving household members and caretakers ensued. Cholera killed 3.4%-8.9% of the populations, with highest mortality among seniors (16%) and lowest in children (2.7%). Transmissibility (R0) was 1.7-2.6 and the serial interval was estimated at 3.7 days (95% confidence interval, 2.9-4.7 days). The case fatality ratio (CFR) was high (54%-68%); using R0 we computed an adjusted CFR of 4%-5%. Conclusions: Short-cycle transmission was likely critical to early secondary transmission in historic Danish towns. The outbreaks resembled the contemporary Haiti outbreak with respect to transmissibility, age patterns, and CFR, suggesting a role for broader hygiene/sanitation interventions to control contemporary outbreaks.

The importance of thinking beyond the water-supply in cholera epidemics: A historical urban case-study.

BACKGROUND: Planning interventions to respond to cholera epidemics requires an understanding of the major transmission routes. Interrupting short-cycle (household, foodborne) transmission may require different approaches as compared long-cycle (environmentally-mediated/waterborne) transmission. However, differentiating the relative contribution of short- and long-cycle routes has remained difficult, and most cholera outbreak control efforts focus on interrupting long-cycle transmission. Here we use high-resolution epidemiological and municipal infrastructure data from a cholera outbreak in 1853 Copenhagen to explore the relative contribution of short- and long-cycle transmission routes during a major urban epidemic. METHODOLOGY/PRINCIPAL FINDINGS: We fit a spatially explicit time-series meta-population model to 6,552 physician-reported cholera cases from Copenhagen in 1853. We estimated the contribution of long-cycle waterborne transmission between neighborhoods using historical municipal water infrastructure data, fitting the force of infection from hydraulic flow, then comparing model performance. We found the epidemic was characterized by considerable transmission heterogeneity. Some neighborhoods acted as localized transmission hotspots, while other neighborhoods were less affected or important in driving the epidemic. We found little evidence to support long-cycle transmission between hydrologically-connected neighborhoods. Collectively, these findings suggest short-cycle transmission was significant. CONCLUSIONS/SIGNIFICANCE: Spatially targeted cholera interventions, such as reactive vaccination or sanitation/hygiene campaigns in hotspot neighborhoods, would likely have been more effective in this epidemic than control measures aimed at interrupting long-cycle transmission, such as improving municipal water quality. We recommend public health planners consider programs aimed at interrupting short-cycle transmission as essential tools in the cholera control arsenal.

Impact of antibiotic restriction on resistance levels of Escherichia coli: a controlled interrupted time series study of a hospital-wide antibiotic stewardship programme.

OBJECTIVES: We evaluated the effect of an antibiotic stewardship programme (ASP) on the use of antibiotics and resistance levels of Escherichia coli using a method that allowed direct comparison between an intervention hospital and a control hospital. METHODS: The study was conducted as a retrospective controlled interrupted time series (ITS) at two university teaching hospitals, intervention and control, with 736 and 552 beds, respectively. The study period was between January 2008 and September 2014. We used ITS analysis to determine significant changes in antibiotic use and resistance levels of E. coli. Results were directly compared with data from the control hospital utilizing a subtracted time series (STS). RESULTS: Direct comparison with the control hospital showed that the ASP was associated with a significant change in the level of use of cephalosporins [-151 DDDs/1000 bed-days (95% CI -177, -126)] and fluoroquinolones [-44.5 DDDs/1000 bed-days (95% CI -58.9, -30.1)]. Resistance of E. coli showed a significant change in slope for cefuroxime [-0.13 percentage points/month (95% CI -0.21, -0.057)] and ciprofloxacin [-0.15 percentage points/month (95% CI -0.26, -0.038)]. CONCLUSIONS: The ASP significantly reduced the use of cephalosporins and fluoroquinolones, with concomitant decreasing levels of E. coli resistance to cefuroxime and ciprofloxacin. The same development was not observed at the control hospital.

Capturing the dynamics of pathogens with many strains.

Pathogens that consist of multiple antigenic variants are a serious public health concern. These infections, which include dengue virus, influenza and malaria, generate substantial morbidity and mortality. However, there are considerable theoretical challenges involved in modelling such infections. As well as describing the interaction between strains that occurs as a result cross-immunity and evolution, models must balance biological realism with mathematical and computational tractability. Here we review different modelling approaches, and suggest a number of biological problems that are potential candidates for study with these methods. We provide a comprehensive outline of the benefits and disadvantages of available frameworks, and describe what biological information is preserved and lost under different modelling assumptions. We also consider the emergence of new disease strains, and discuss how models of pathogens with multiple strains could be developed further in future. This includes extending the flexibility and biological realism of current approaches, as well as interface with data.

Effects of host heterogeneity on pathogen diversity and evolution.

Phenotypic variation is common in most pathogens, yet the mechanisms that maintain this diversity are still poorly understood. We asked whether continuous host variation in susceptibility helps maintain phenotypic variation, using experiments conducted with a baculovirus that infects gypsy moth (Lymantria dispar) larvae. We found that an empirically observed tradeoff between mean transmission rate and variation in transmission, which results from host heterogeneity, promotes long-term coexistence of two pathogen types in simulations of a population model. This tradeoff introduces an alternative strategy for the pathogen: a low-transmission, low-variability type can coexist with the high-transmission type favoured by classical non-heterogeneity models. In addition, this tradeoff can help explain the extensive phenotypic variation we observed in field-collected pathogen isolates, in traits affecting virus fitness including transmission and environmental persistence. Similar heterogeneity tradeoffs might be a general mechanism promoting phenotypic variation in any pathogen for which hosts vary continuously in susceptibility.

Five challenges in modelling interacting strain dynamics.

Population epidemiological models where hosts can be infected sequentially by different strains have the potential to help us understand many important diseases. Researchers have in recent years started to develop and use such models, but the extra layer of complexity from multiple strains brings with it many technical challenges. It is therefore hard to build models which have realistic assumptions yet are tractable. Here we outline some of the main challenges in this area. First we begin with the fundamental question of how to translate from complex small-scale dynamics within a host to useful population models. Next we consider the nature of so-called "strain space". We describe two key types of host heterogeneities, and explain how models could help generate a better understanding of their effects. Finally, for diseases with many strains, we consider the challenge of modelling how immunity accumulates over multiple exposures.

Nine challenges for deterministic epidemic models.

Deterministic models have a long history of being applied to the study of infectious disease epidemiology. We highlight and discuss nine challenges in this area. The first two concern the endemic equilibrium and its stability. We indicate the need for models that describe multi-strain infections, infections with time-varying infectivity, and those where superinfection is possible. We then consider the need for advances in spatial epidemic models, and draw attention to the lack of models that explore the relationship between communicable and non-communicable diseases. The final two challenges concern the uses and limitations of deterministic models as approximations to stochastic systems.

Modeling infectious disease dynamics in the complex landscape of global health.

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Prenatal influenza exposure and cardiovascular events in adulthood.

OBJECTIVES: This study examined the association between prenatal exposure to pandemic influenza and cardiovascular events in adulthood. DESIGN: Using Danish surveillance data to identify months when influenza activity was highest during three previous pandemics (1918, 1957, and 1968), persons were defined as exposed/unexposed based on whether they were in utero during peak months of one of the pandemics. Episodes of acute myocardial infarction (MI) and stroke were identified in the Danish National Registry of Patients covering all Danish hospitals since 1977. SETTING/SAMPLE: Information from Danish national registries on all persons with a Civil Personal Registry number and birthdates in 1915 through 1922, 1954 through 1960, and 1966 through 1972 was collected. MAIN OUTCOME MEASURES: Crude incidence rate ratios (IRRs) were calculated per pandemic. Generalized linear models were fit to estimate IRRs adjusted for sex. RESULTS: For acute MI, sex-adjusted IRRs for persons in utero during peaks of the 1918, 1957, and 1968 pandemics, compared with those born afterward, were 1·02 (95% confidence interval (CI): 0·99, 1·05), 0·96 (95% CI: 0·87, 1·05), and 1·18 (95% CI: 0·96, 1·45), respectively. For stroke, the corresponding IRRs were 0·99 (95% CI: 0·97, 1·02), 0·99 (95% CI: 0·92, 1·05), and 0·85 (95% CI: 0·77, 0·94), respectively. CONCLUSIONS: There was generally no evidence of an association between prenatal influenza exposure and acute MI or stroke in adulthood. However, survivor bias and left truncation of outcomes for the 1918 pandemic are possible, and the current young ages of persons included in the analyses for the 1957 and 1968 pandemics may warrant later re-evaluation.

Immune boosting explains regime-shifts in prevaccine-era pertussis dynamics.

Understanding the biological mechanisms underlying episodic outbreaks of infectious diseases is one of mathematical epidemiology's major goals. Historic records are an invaluable source of information in this enterprise. Pertussis (whooping cough) is a re-emerging infection whose intermittent bouts of large multiannual epidemics interspersed between periods of smaller-amplitude cycles remain an enigma. It has been suggested that recent increases in pertussis incidence and shifts in the age-distribution of cases may be due to diminished natural immune boosting. Here we show that a model that incorporates this mechanism can account for a unique set of pre-vaccine-era data from Copenhagen. Under this model, immune boosting induces transient bursts of large amplitude outbreaks. In the face of mass vaccination, the boosting model predicts larger and more frequent outbreaks than do models with permanent or passively-waning immunity. Our results emphasize the importance of understanding the mechanisms responsible for maintaining immune memory for pertussis epidemiology.

Modelling seasonal variations in the age and incidence of Kawasaki disease to explore possible infectious aetiologies.

The average age of infection is expected to vary during seasonal epidemics in a way that is predictable from the epidemiological features, such as the duration of infectiousness and the nature of population mixing. However, it is not known whether such changes can be detected and verified using routinely collected data. We examined the correlation between the weekly number and average age of cases using data on pre-vaccination measles and rotavirus. We show that age-incidence patterns can be observed and predicted for these childhood infections. Incorporating additional information about important features of the transmission dynamics improves the correspondence between model predictions and empirical data. We then explored whether knowledge of the age-incidence pattern can shed light on the epidemiological features of diseases of unknown aetiology, such as Kawasaki disease (KD). Our results indicate KD is unlikely to be triggered by a single acute immunizing infection, but is consistent with an infection of longer duration, a non-immunizing infection or co-infection with an acute agent and one with longer duration. Age-incidence patterns can lend insight into important epidemiological features of infections, providing information on transmission-relevant population mixing for known infections and clues about the aetiology of complex paediatric diseases.

Natality decline and miscarriages associated with the 1918 influenza pandemic: the Scandinavian and United States experiences.

BACKGROUND: Although pregnancy is a recognized risk factor for severe influenza infection, the effect of influenza on miscarriages and births remains unclear. We examined the relationship between influenza and birth rates during the 1918 pandemic in the United States, Denmark, Sweden, and Norway. METHODS: We compiled monthly birth rates from 1911 through 1930 in 3 Scandinavian countries and the United States, identified periods of unusually low or high birth rates, and quantified births as "missing" or "in excess" of the normal expectation. Using monthly influenza data, we correlated the timing of peak pandemic exposure and depressions in birth rates, and identified pregnancy stages at risk of influenza-related miscarriage. RESULTS: Birth rates declined in all study populations in spring 1919 by a mean of 2.2 births per 1000 persons, representing a 5%-15% drop below baseline levels (P < .05). The 1919 natality depression reached its trough 6.1-6.8 months after the autumn pandemic peak, suggesting that missing births were attributable to excess first trimester miscarriages in ∼1 in 10 women who were pregnant during the peak of the pandemic. Pandemic-related mortality was insufficient to explain observed patterns. CONCLUSIONS: The observed birth depressions were consistent with pandemic influenza causing first trimester miscarriages in ∼1 in 10 pregnant women. Causality is suggested by temporal synchrony across geographical areas.