on 19-Nov-2019 (Tue)

The study by Reich et al. (13) points at an overall degradation of disease forecasts at the 4-wk time horizon, beyond which the average of historical incidences (i.e., a null model) becomes the most reliable option for prediction.

From a public health standpoint, a longer horizon, in the order of 2 mo or more, would be particularly useful to ramp up interventions and adjust hospital surge capacity.

Ideally, even longer timescales should be considered so that the prediction of epidemic intensity (epidemic size) and severity (total number of hospitalizations and deaths) aligns with the vaccine manufacturing process.

Wearing et al. (2005) use analytical methods to show that

(i) ignoring the latent period

or

(ii) making the common assumption of exponentially distributed latent and infectious periods (when including the latent period)

always results in underestimating the basic reproductive ratio of an infection from outbreak data

The dynamical consequences of differences in the distribution of infectious and latent periods have received some attention over the past two decades. It has been shown, for example, that the precise distribution of the infectious period has no qualitative effects on the asymptotic values or properties of the system [21,24], though perturbations to the endemic equilibrium take longer to die out as n increases [22,26]. When contact rates vary seasonally, for example, to mimic the aggregation of children in schools [28,29], changes in p(t) are known to have important consequences for the persistence likelihood of infections [25,26,30]. An issue that has received surprisingly little attention, despite its obvious applicability to emerging infections and possible ‘‘ deliberate exposure,’’ is the influence of latent and infectious period distributions on the invasion dynamics of an infection into a largely susceptible population.

2019 Chretien et al: "it is not sufficient merely [for a model] to predict an event; the likelihood of that prediction should also be ascribed"

In this Review, we start with an examination of real-time outbreak response using the West African Ebola epidemic as an example. Here, the challenges range from underreporting of cases and deaths, and missing information on the impact of control measures to understanding human responses.

For infectious agents important to public health, a series of principles has emerged for modeling infection dynamics (Table 1 and Box 4). The basic reproduction number R₀ ,for example, is a central concept characterizing the average number of secondary cases generated by one primary case in a susceptible population. This concept highlights what must be measured to interpret observed d isease patterns and to quantify the impact of selected control strategies (Fig. 1)

Several classes of model are used, spanning the spectrum of information available. At one end of the range are detailed individual-based simulation models, where large numbers of distinct individual entities (with their own characteristic traits such as age, spatial location, sex, immune status, risk profile , or behavior pattern) are described in interaction with each other, possibly in a contact network, and with the infectious agent. At the other end are compartmental models where no individuals are recognized, but only states for individuals (for example: susceptible, infectious, immune) aggregated into compartments where every one has the same average characteristics and where interaction is typically uniform (everybody interacts with everybody else). Such models do not describe the disease history of single individuals, but rather the time evolution of aggregated variables, such as the number of individuals that are currently susceptible. Mathematical models can have both mechanistic parts in their description, based on assumptions about biological mechanisms involved, and more phenomenological parts, where there is a statistical or presumed relation between variables, without clear assumptions from which this relation can be derived. An example of the former is the assumption of mass action to describe interaction betw een individual hosts; an example of the latter is an empirical relation between the length of an infectious period in a mosquito and environmental temperature.

Although sequence data are extremely valuable, to link these data fully to disease dynamics, it will be important to determine how sequence changes affect functions related to pathogen fitness, such as replication rate, transmissibility, and immune recognition. Molecular epidemiological studies often treat pathogen genetic variation as simply reflecting the underlying transmission process, whereas in reality such variation may play an important role in determining transmission dynamics, as exemplified by escape from herd immunity by influenza A virus (31)

Multiple agents infecting the same host individual have been shown to influence each other by increasing or decreasing susceptibility and/or infectivity of that individual, thereby influencing the population dynamics of these agents in ways that we have yet to explore and understand (34, 35)

Increasing sophistication of contact network models (42), together with data on epidemiological contacts, creates opportunities for understanding and controlling transmission at a fundamental level (43) and opens up the possibility of independent study of relevant social factors (10). Recent years have seen exciting developments in the measurement of contact patterns and “who might infect whom” through advances in individual electronic identification technology.

Although the rapid expansion of infectious disease models and their application over the past decade has coincided with an increase in open access data sets available from a variety of sources, progress in data capture needs to be accelerated. Although some of these technologically advanced data streams have been incorporated into models—for example, to track the incidence of influenza in the United States (72), to elucidate the spatial dynamics of measles and malaria in Africa (53, 73), and to chart the spread of dengue globally (74)

Given the mismatch with regions where most expertise on infectious disease dynamics is concentrated, it is important to empower local scientists and policy-makers, in regions where the burden of disease is heaviest, about the problems facing their own countries and the consequences of local actions. It is essential to make expertise, data, models, statistical methods, and software widely available by open access. There are several initiatives (e.g., Thiswormyworld.org, Garkiproject.nd.edu, EDENextdata.com, the Humanitarian Data Exchange (HDX), and the Malaria Atlas Project), but more needs to be done.

The basic reproduction number R₀ ,for example, is a central concept characterizing the average number of secondary cases generated by one primary case in a susceptible population.

Hill et al. (2019) amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season.

Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for:

• epidemiological processes,
• virological sample positivity,
• vaccine uptake and efficacy attributes, and
• general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC).

We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition.

Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions.

Influenza vaccination can offer some protection against seasonal influenza infection for the individual, while contributing to reduced risk of ongoing transmission via establishment of herd immunity [3, 4]. Influenza vaccines are designed to protect against three or four different influenza viruses; two influenza A viruses (an A(H1N1)pdm09 subtype and A(H3N2) subtype) and either one or two influenza B viruses (covering one or both of the B/Yamagata and B/Victoria lineages).

Here, we present a multi-strain, non-age structured, susceptible-latent-infected-recovered (SEIR) type transmission model for influenza incorporating a mechanism to link prior influenza season epidemiological outcomes to immunity at the beginning of the following influenza season. Incorporation of a mechanism for the building and propagation of immunity facilitates investigation of the impact of exposure in the previous influenza season, through natural infection or vaccination, on the disease transmission dynamics and overall disease burden in subsequent years.

Accordingly, we sought insights to aid understanding of the longer-term dynamics of the influenza virus and its interaction with immunity at the population level. In this study, we examine the contribution of the differing sources of immunity propagation between years on seasonal influenza transmission dynamics.

To this end, we amalgamate multiple sources of epidemiological and vaccine data for England covering the last decade, and fit model outcomes to the available longitudinal data of seasonal rates of general practice (GP) consultations for influenza-like-illness (ILI), scaled by virological surveillance information.

We demonstrate that natural infection plays a more prominent role in propagation of immunity to the next influenza season compared to residual vaccine immunity. We conclude by inspecting forward projections under disparate vaccine efficacy assumptions to determine long-term patterns of seasonal influenza infection.

Hill et al. (2019) used data subsequent to the 2009 pandemic for England, and developed a dynamic multi-strain SEIR-type transmission model for seasonal influenza, explicitly incorporating immunity propagation mechanisms between influenza seasons. With a view to minimising the number of independent parameters, we fit a parsimonious mechanistic model to seasonal-level data on strain competition. In spite of the multi-strain complexity and time scale of the study period (six influenza seasons), predictions from the model attain a strong qualitative resemblance (in terms of strain composition and overall quantity of GP consultations) to the empirical subtype data from England.

We attribute much of the discrepancies to the homogeneous way in which we have treated immunity propagation, as in practise this is driven by complex patterns of waning and cross immunity as well as somewhat irregular genetic drift.

Inferred transmissibility of type A influenza strains exceeded those of type B, which reflects type A being the predominant class of influenza virus in circulation over the studied time period [26]. Moreover, concentrating on the parameters relevant to immunity propagation, we uncover evidence against vaccination stimulating similar long-term immunity responses as for natural infection. These conclusions corroborate previous immunological studies showing that infection with influenza virus can induce broader and longer-lasting protection than vac- cination [37, 48, 49], and authenticate prior work signalling that vaccine-mediated immunity rapidly wanes [50]. There are also indications that prior natural infection boosts vaccine responses against antigenically drifted strains, whereas prior vaccination does not [51]. The clinically observed impact of prior infection for enhancing vaccine efficacy was long-lasting, which may be used to instruct further model refinements.

In the case of finding minimal support for carry over cross-reactivity protection between influenza B lineages (parameter b), we have reported the best parameter fits under the modelling assumptions made. Considered collectively, the inferred parameter fits signify that, at the population level, a combination of transmissibility rates, season-specific ascertainment probabilities and modifications to susceptibility by lineage-specific immunity propagation (parameter a) and within-season vaccination are sufficient to obtain the closest correspondence to the data under the modelling assumptions, without requiring a prominent amount of influenza B cross-reactive immunity propagation. In other words, if there was presence of propagation of protection between influenza B lineages, it was dwarfed by other processes within the biological system.

The effects that consequential increased media coverage of an outbreak have on transmission dynamics can be complex [55], with the perception of the disease conceivably changing among the population [56]. Chiefly in this instance, for those infected and symptomatic the propensity to consult a GP may have been abnormally raised, which would result in an increased ascertainment probability relative to prior influenza seasons.

Spanning our pessimistic to optimistic vaccine efficacy scenarios, Hill et al. (2019) generally attained a model predicted incidence (per influenza season) of 30-50%. With volunteer challenge studies indicating that the majority of those infected by influenza are asymptomatic [57], plus only around 10% of those with ILI thought to consult a GP [58, 59], our model predicted influenza incidence pairs satisfactorily with previous influenza burden estimates covering England [60]. Additionally, influenza burden estimates in the USA (provided by the CDC) covering the 2017/18 and 2018/19 influenza seasons ascribe more than 48.8 million and 37.4-42.9 million influenza associated illnesses respectively, which is in the realms of 10-15% of the national population suffering symptomatic infection [61, 62].

The development of the influenza transmission model presented here was built upon a col- lection of simplifying assumptions. A limitation of the model is fixing the influence of prior exposure history to a single influenza season. Studies of repeated vaccination across multiple influenza seasons suggest that vaccine effectiveness may be influenced by more than one prior influenza season [65].

Though we did include a parameter corresponding to immunity proliferating across influenza B lineages between influenza seasons, we did not include influenza A heterosubtypic immunity amongst the immunity propagation mechanisms.

Recent work has found H1 and H3 influenza infection in humans induces neuraminidase-reactive antibodies displaying broad binding activity spanning the entire history of influenza A virus circulation (in humans) [37]; these developments motivate continued work and data acquisition to elicit the timespan over which cross-reactive antibodies (arising from natural influenza infection) may significantly reduce susceptibility to unrelated influenza A subtypes and influenza B lineages.

Hill et al. (2019) assumed the mechanism underpinning propagation of immunity from influenza vaccination in the previous influenza season behaved linearly. We recognise a linear dependency is a strong generalisation. Given the vaccine-induced protection to an influenza virus strongly depends on the level of mismatch of the strain contained in the vaccine and the circulating strain, a vaccine that is very protective in the current influenza season might be ineffective the following year due to the influenza virus undergoing antigenic mutation.

for simplicity we assumed each influenza season was initialised with a low level of all subtypes; in practise the subtypes seeding each epidemic will be contingent on the dynamics in the rest of the world

we assumed the value of the ascertainment probability to be constant over the course of the influenza season

HIll et al. (2019) find that susceptibility in the next influenza season to a given influenza strain type is modulated to the greatest extent through natural infection by that strain type in the current influenza season, with residual vaccine immunity having a lesser role and inconsequential support for carry over type B cross-reactivity.

The study by Reich et al. (13) points at an overall degradation of disease forecasts at the 4-wk time horizon

The study by Reich et al. (13) shows beyond 4-wks, the average of historical incidences (i.e., a null model) becomes the most reliable option for prediction.

The branching process is the linear approximation of the SIR stochastic process near the disease-free equilibrium.

For a few initial infectious individuals, the branching process either grows exponentially or hits zero.

The first step in converting from an ER schema to a dimensional schema is dividing the ER schema into separate business processes. Each of these business processes will be modeled into a separate star schema.

The next step is to declare the granularity of the business process (for example, a single flight or one salary payment). We then group the measures that are used in the business process into a single fact table.

After that, the remaining tables are flattened into dimension tables and directly linked to the fact table using a single key. It is possible for the same dimension table to be used in multiple star schemas. This is called conformed dimension. For example, the employee dimension can be used in the context of Airline Operations as well as in the payroll context.

The associative data model can be said to be equal in structure to a dimensional model. However, a data model of any type in QlikView becomes an associative data model because it not only contains the different source tables from which the charts get the data, but also keeps them associated in a way which allows the QlikView document and its users to consume information and aggregate data cross dimensionally in any possible way. In a QlikView data model, all of the field values from all of the tables in the model are automatically associated among themselves based purely on the field names.

These three fields are stored in three different dimension tables in the data model, and the fact that they are associated allows QlikView to naturally perform this cross-dimensional reference and support the associative analysis we just described. This is shown in the following screenshot:

In an associative data model, any field can act as a dimension in a chart. They can all be used within expressions to aggregate their data too

Renaming fields There are two main reasons for a developer to rename a field: • To ensure that two tables are associated through the correct fields when originally these two tables did not share a field with the same name, but a link does in fact exist between them. • To prevent unwanted associations between tables when they share a field with the same name but that field does not actually represent the link between them

Renaming fields with the Qualify statement The Qualify keyword can be used to qualify field names with their corresponding table name, which basically renames the specified fields in the form of tablename. fieldname , thus ensuring no unwanted associations are created. Let's look at our previous example in which we needed to rename the Code and Description fields. We can rename these fields by using the Qualify keyword as follows: Qualify Code, Description; [Origin Airports]: LOAD Code, Description FROM [..\Data Files\QVDs\Airport Descriptions.qvd] (qvd); The above Load statement will result in a table with two fields: Origin Airports. Code and Origin Airports.Description. As you can see, we have specifically entered, as the Qualify statement parameter, the two fields which we want to rename. In some cases, we might need to rename a long list of fields, making it impractical to list them all in a Qualify statement. Luckily, the Qualify statement allows the use of wildcard characters in the fieldlist parameter. For example, we can use a star symbol to specify that all subsequently loaded fields should be qualified. We can also combine the star symbol with a string or with a question mark symbol (another wildcard character) to specify that a set of fields that match a given criteria are to be qualified. For instance: • Qualify Code, Description;: This command will only qualify fields named Code or Description • Qualify "*ID";: This command will qualify all fields whose name ends with ID • Qualify *;: This command will qualify all fields that are loaded from that point forward

Sometimes, the Qualify feature is required to be activated for only part of the script but then should be turned off after loading certain tables. To do that, we can simply use the Unqualify statement, specifying the field names in the same way as described previously. In some cases, we are required to turn on qualification for all fields except one or two (for example, the key fields). To accomplish that, the Qualify and Unqualify instructions can be used in conjunction, like in the following example: Qualify *; Unqualify Key_Field1, Key_Field2; The above combination of instructions will cause all fields loaded from that point forward to be qualified with their corresponding table name, except the fields named Key_Field1 and Key_Field2. This is useful when we want to ensure key fields are not affected by the Qualify instruction. Remember that a Qualify instruction can be turned off at any point in the script with a corresponding Unqualify statement

The presence of synthetic keys in a data model can cause the application to have slow response time and sometimes even consume all available resources. Therefore, they need to be avoided when possible. There are several methods we can use to remove synthetic keys: • We can rename those fields that are a part of the synthetic key but should not be a part of the association between the two tables. • We can remove conflicting fields from one of the two tables. To remove a field, we just erase the corresponding line of code from the Load script. • We can create an explicit complex key with the concatenation of all common fields that actually represent the link between the two tables. ° After creating the new complex key, we can remove the conflicting fields from either table.

Dealing with Synthetic Keys – Flow Chart.

...

Adding a delimiter to concatenated fields It's always a good idea to add a separator between fields when concatenating them to ensure data consistency. For example: Year & ' | ' & Month as YearMonth

Dealing with circular references Similar to how synthetic keys are created, a circular reference can also be the result of unwanted associations in our data model and, as such, they can be fixed using the same principles described earlier. But before getting into how to solve them, let's first see what they are. We can think of the data model as a map that shows the paths through which we can walk to get from one point to another. For instance, considering the data model seen in the previous section, the only path to get from the fact table to the Calendar table is the YearMonth route. This means in order to get there you must know what Month and Year you are looking for in the Calendar table. However, when the data model becomes more and more complex, with a larger number of tables, and more and more destination points, we might also get to a point where we have more than one route connecting point A to point B. All roads lead to Rome, they say. Well, in our case, we must always have one road between any two points. Otherwise, we would be having a circular reference.

Field information When the mouse cursor hovers over a field name within a table, it gets even more interesting. The following information is shown in the previous screenshot: • The name of the field. Optionally, if the field is a key field, a qualifier is shown enclosed in square brackets. This qualifier indicates the following levels of key quality: ° [Perfect Key] indicates that every row contains a key value, and that all of these key values are unique. At the same time, the field's subset ratio is 100 percent. This qualifier should be seen in dimension tables, where every key should uniquely identify a single record. ° [Primary Key] indicates that all key values are unique, but not every row contains a key value or the field's subset ratio is less than 100 percent. ° [Key] indicates that the key is not unique. This qualifier is usually seen in fact tables, where the same dimension value may be associated with many different facts.

• Information density of the field, which indicates the percentage of rows that contain a non-null value. • Subset ratio, which shows the percentage of all distinct values for a field in the table compared to all the distinct values for that field in the entire data model. It is only relevant for key fields since they are present in multiple tables and do not all share the same value. Subset ratios can be used to easily spot problems in key field associations. For example, when the combined total of subset ratios for multiple tables is 100 percent, this may indicate that there are no matching keys between these tables. • Tags, which show the tags applied to the field. Some of these, such as tags that indicate if the field is a key field or tags indicating the data type of the field, are automatically generated. Other tags can be manually applied. • Optionally, any comment set on the field is also shown.

Of special interest is the drop-down box that switches the view between Internal Table View and Source Table View. Internal Table View is the default option and shows how the data is stored in QlikView. If synthetic keys are created, they are shown in this view. However, Source Table View shows how QlikView reads the data, and when synthetic keys are present in the model they are not shown in this view. Instead, multiple connectors between tables are displayed.

When using the View | Resize Window functionality, it is always advisable to also check on the target environment to see if there will be any toolbars or other objects eating into the screen real estate.

The Current Selections Box QlikView lets us select data in many different ways: listboxes, clicking in charts and entering search terms, just to name a few. While this is incredibly flexible, it can also become hard to see which information is actually selected at any given moment. Fortunately, QlikView has an option to show the user exactly which selections are currently applied to the data: the Current Selections dialog. To open this dialog, we simply need to press Ctrl + Q or select View | Current Selections from the menu bar. This floats the Current Selections dialog window on top of our worksheet.

Moving objects Objects (or a selected group of objects) can be moved by clicking on the caption bar and dragging them to the desired location. Objects without a caption (for example, the listboxes showing Year, Quarter, and Month that we created earlier) can be dragged by holding Alt and clicking and dragging anywhere on the object. This method also works for objects with a visible caption, and even for objects where Allow Move/Size is disabled. Holding Ctrl while pressing the arrow keys moves the active object(s) 1 pixel at a time. Use Ctrl + Shift to move them in 10-pixel steps.