Infectious disease pandemic planning and response
Proper planning is an important part of reducing the chances of potentially catastrophic infectious disease from spreading in society. Such a statement is easier said than done.
During a pandemic, preparatory readiness is delayed by doubts and disbelief which wastes valuable time. As well, the lack of prior experience leaves uncertainty and no guidelines for optimum decision making.
In the H1N1 pandemic in 1918 millions of people died worldwide.
The decimation was simultaneous with changes in the environment, before and after.
In the meantime, vaccines and antivirals are touted today as thorough they reduce the impact of any type of epidemic disease related to a virus- including past viruses. However, this is not consistent with the nature of virus ecology. Virus ecology helps viruses to change whenever the host has changed. Vaccines can not block or destroy viruses, including in 100% vaccination of animals and humans. In other words, outbreaks epidemics and pandemics will always occur, including with what are described as new strains of the same virus, in order to fulfil its ecological mission and goal.
Many cities are connected by air travel, so there are chances that pathogens can spread by air travel.
However, this has not been the case between species but only between confined and massively-reproduced unsustainable animals, as in the H1N1 pandemic that hit the nation in 2009 and spread rapidly to 74 countries in 4 months. This is because animal farming is faster than sustainable farming and animals on farms which are slaughtered trigger the attraction of deadly viruses when there is a suitable environmental condition before-, during or after.
A mathematical model with statistics can be helpful in planning strategies to deal with a pandemic and how to respond but not always. We see mistakes in models that forecasted lockdowns in Ile, France would help reduce spread. In fact, this was not so . The models used statistical inferences based on a constant linear rate and no relationship to virus multiplication in specific kinds of hosts that were ingested by their human victims. The rate of spread was also not correlated to environmental conditions that are a necessary part of virus multiplication. And so France used its incorrect model-based statistical forecast to carry out an ineffective lockdown that did not stop outbreaks. If anything, the severity of the subsequent outbreaks increased during lockdowns which somehow inspired health ministers to suggest more aggressive isolation including of children from their families and more surgical day-long masks, including those that have evidence to trigger lung carcinoma and while virus density concentrations are thick enough to pass through fabric, skin, brick, rubber and even plastic wrapping. The problem with statistics to-date in the publications shows no relationship to virus ecology and for that reason, it is driven only by transient detection of disease symptoms, typically much too late or with tests of saliva that are also not related to the location of SARS infectivity, the small intestine. When or where the next pandemic will occur is part of our goal so that each nation has the opportunity to know 7-10 days in advance where the next outbreak will register and to fight it effectively or even with laws to hold liable sources critically responsible. As part of a toolkit, forecasting epidemic outbreaks can be used to provide awareness and better understanding of the nature of the virus path in humans and to environmental and social sustainability.
Other interesting considerations: How does the model help in the current pandemic response policy?
In general, the pandemic preparedness and response models produced from these efforts can be broadly classified into two groups:
those aiming to inform situational awareness and
those aiming to understand the merits of possible interventions.
Modeling data can help to fight pandemics that are of known diseases. In 1973, Fox and partners portrayed the utilization of pandemic recreation models which were dependent on microorganism attributes similar to the ones seen in 1957 H2N2 and 1968 H3N2 to investigate the expected effect of mass immunization and school terminations. Environmentally, however, the models were not repeatable.
Many years later, modelers and strategy planners tried to use their strategies to reach flu A(H1N1)pdm09. By utilizing observation frameworks and computational force not accessible to their archetypes in 1968, a plethora of new model-dynamic concepts emerged. Some were created merely to give continuous appraisals of the pandemic effect level and adequacy of the best considered control measures.
It was thought that the development of flu A(H1N1)pdm09 vitally depended on versatility to advancing pandemic situations that were unknown at the time. Numerous international researchers tried to investigate the pathways that created a virus-outbreak situation emergent at vulnerable locations. Like all epidemics, the initial outbreak depended on infection. In this case, it depended on the more deadly HPAI H5N1 infections. By contrast, A(H1N1)pdm09 was relatively gentle and as such, it inspired nations to agree that a quick change was needed in the environment in order to prevent further outbreaks.
Other models, known as expert systems, triggered some suspicions about the models utilized in 2009 which had relied on pre-pandemic related models, but that did not show the same output forecasts. Since 2009, models have been progressively upheld as the ideal way to simplify monitoring and pandemic readiness planning. But has it? Today, in 2021, worldwide virus-related cancer and infection rates continue to rise, including the scourges of extreme intense respiratory conditions (SARS, 2002–2003) correlated to the intense and threatening effects of environmental climate deterioration. The rise of profoundly pathogenic avian flu (HPAI) infection H5N1 (2003), and the West African Ebola infection sickness plague (2013–2016), have also additionally not helped advances in pandemic readiness and reaction abilities- despite the promotion of models and their artificial intelligence.