**3. Results**

## **3.1 Model simulation**

We simulated the model using the Runge–Kutta method via the function ode45 in MATLAB. The time unit is day. We assume that the epidemic started with 100

#### **Figure 5.**

*Simulation of (from left to right) the disease prevalence, the proportion of those practicing social isolation, and per-capita GDP (\$1000). The human behavior parameter values are ξ* ¼ 0*, c*<sup>1</sup> ¼ 10*, c*<sup>2</sup> ¼ 100, *and c*<sup>3</sup> ¼ 5 *for figure panels (a-b), ξ* ¼ 0*, c*<sup>1</sup> ¼ 100*, c*<sup>2</sup> ¼ 1000, *and c*<sup>3</sup> ¼ 50 *for figure panels (d-f), ξ* ¼ 0*:*1*, c*<sup>1</sup> ¼ 100*, c*<sup>2</sup> ¼ 1000, *and c*<sup>3</sup> ¼ 50 *for figure panels (g-i), and ξ* ¼ 0*:*5*, c*<sup>1</sup> ¼ 100*, c*<sup>2</sup> ¼ 1000, *and c*<sup>3</sup> ¼ 50 *for figure panels (j-l). Prevalence is shown as per 1.*

exposed, 50 asymptomatic and 30 infected individuals in a population of size 11,000,000.

Simulations were performed with values given in **Table 1**. In particular, when there is no pandemic fatigue (*ξ* ¼ 0), we found that people can adhere closely to social isolation (policy compliance), resulting in a curb in the disease prevalence, and inflecting and accepting a significant economic burden (at *c*<sup>1</sup> ¼ 10, *c*<sup>2</sup> ¼ 100, and *c*<sup>3</sup> ¼ 5), see **Figure 5 (a)**, **(b)**, and **(c)**. We found also some fluctuations in prevalence occurring from human behavior (at *c*<sup>1</sup> ¼ 100, *c*<sup>2</sup> ¼ 1000, and *c*<sup>3</sup> ¼ 50) (**Figure 5(d)**) and the choice between performing social distance (policy compliance) (**Figure 5(e)**) or ignoring public health directives to maintain economic benefits (i.e. no loss of personal income) (**Figure 5(f)**). The competing interests result in waves of the disease due to changing population level of social isolation versus economic loss from compliance.

In the presence of pandemic fatigue (*ξ* ¼ 0*:*1), and at the same perceived costs (*c*<sup>1</sup> ¼ 100, *c*<sup>2</sup> ¼ 1000, and *c*<sup>3</sup> ¼ 50), fluctuations continue to occur with a smaller magnitude second wave having a shorter inter-wavelength due to the reduced periods of compliance to social isolation (**Figure 5(g)–(i)**). That is, at a high brunt of economic loss and pandemic fatigue, people might be seen to abandon social isolation which results in a continuation in the spread of the disease, even when fear of death was also high. Increasing the pandemic fatigue rate (*ξ* ¼ 0*:*5), results in faster decline in policy compliance and a relatively larger epidemic that does not seem to be abated nor fluctuating.

In all of the cases, the per-capita GDP dwindles fast during the waves of the epidemic and slows down as the waves subside, due to the availability of labor and the decreased hospitalizations.

## **4. Discussion**

#### **4.1 Public health guidance and human choice as influencers**

Human choice is an important influencer on disease dynamics, and it is dependent on cultural, social and economic factors that might lead to lack of choice. Our model results (**Figure 5**) exhibit that risk of infection, fear of death and the effect of economic loss are important factors as they influence the behaviors of individuals in both lower and higher GDP countries. In lower income countries, an individual's daily wages depend on socioeconomic growth and GDP of the population. The majority of the population in low-income countries survive at or below the poverty line. The World Bank reports there are 33 countries with one-third of the population below the extreme poverty line (\$1.90 international dollars/day income) and 69 countries with more than half their population living on less than \$5.50 international dollars/day. The definitions of the poverty line vary considerably among nations, however, according to the World bank there are 23 countries with 50% or more of the population living below the nationally designated poverty line deemed appropriate - as defined by its own authorities [42]. The low-income countries include many African countries, Latin American countries (Guatemala, Honduras) or areas suffering military conflicts (Afghanistan, Yemen).

Thus, even small changes in income and GDP will be perceived as a larger income shock to individuals living near or below the poverty line. Individuals with very little capacity will ignore pandemic social distancing directives quicker than those with higher capacity, otherwise they will not have money for day-to-day food and basic necessities.

### *Human Cultural Dimensions and Behavior during COVID-19 Can Lead to Policy Resistance… DOI: http://dx.doi.org/10.5772/intechopen.96689*

The perceived relative economical loss (*c*3) displays sensitivity of the society to the change in the GDP. If a country is affluent (as reflected by its higher GDP) then *c*<sup>3</sup> must be of a small value. These countries are less sensitive to any drop, or relative drop, in their GDP. Countries with greater capacity are able to erect more stringent and additional Swiss Cheese Model safeguards. Low-income GDP countries are more sensitive to the changes in the economic cost, thus their *c*<sup>3</sup> value will be larger. This results in a fluctuation in human behaviors in relation to the economical cost which leads to waves of infections. During a pandemic, social isolation invoked by public health results in a decline in the economy and personal incomes but when the disease transmission (or the perception of disease transmission and risk) wanes individuals with lower capacity will relax their social distancing efforts and change behaviors, returning to work. It results in a resurgence in infectious disease case numbers, which in turn, often results in public health oversight increasing social isolation measures. This effect was observed during the "second wave" of COVID-19 as relaxed NPI measures resulted in a resurgence of detected positive cases in the EU, Africa, Asia, North America and South America [43–45].

## **4.2 Efficacy, media amplification, and fear as policy resistance influencers**

Policy resistance is often cast as a conflict between the Nash equilibrium and the *social optimum* coverage [46]. This can be thought of as the tendency for interventions to be defeated by the system's response to the intervention itself. The role of fear and fatigue in compliance with policy can lead to resistance. Fear as a construct can be driven by media coverage.

Previous coronavirus outbreaks Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) displayed an amplification of risk perception due to media coverage of the outbreaks [47, 48]. Studies affirm that individuals obtain their news about health and medicine from both mass and social media sources. Daily newspapers, TV channels are one of the biggest influencers of public perceptions of risk. The media plays an important role informing individuals about health risks, but it can also distort perceptions through social amplification of risk. The Social Amplification of Risk Framework (SARF) describes the process where some hazards and events become the focus of intense social and political concern and activity (amplification). This occurs even though experts and risk assessment can establish that the risk is of a relatively low probability, while other potentially more serious events receive comparatively little public attention (attenuation). Media coverage can magnify and change perceptions of risk. The alteration of risk by social amplification creates secondary effects such as stigmatization (of people, places, objects, technologies, and ideas), economic losses, and changes to regulatory oversight due to mass distortion of public risk perception [49, 50].

The efficacies of social distancing and media coverage causing amplification of risk perceptions during COVID-19 are crucial in developing policy acceptance or resistance. In many countries public health risk communication promoted a collectivist and altruistic approach while in other countries policy resistance arose to NPIs through social media. Evidence suggests that belief in conspiracy theories undermines engagement in pro-health behaviors and support for public health policies [51].

For example, in the USA expert messaging carried out by the US CDC regarding mask wearing to protect vulnerable individuals in society became co-opted by social media's distortion of risk (ineffectiveness of masks, lowered perception of SARS-CoV2 infection risk, and as an infringement of personal choice) [52]. Under our

model social media misinformation regarding the risk factors can alter the effective transmission rate through the proportion 1 � *x* of those individuals disregarding mask wearing and social distancing.

### **4.3 Pandemic fatigue and policy resistance as an influencer**

Pandemic fatigue is recognized by the WHO to be natural and expected and is manifested through the decline in motivation of people to adhere to the recommended protective behaviors [53]. It is believed that fatigue emerges gradually [54] and is affected by a number of emotions, experiences and perceptions as well as the demographic, socio-economical, cultural, structural and legislative environment [55, 56]. During those periods, people will perceive personal, social and economic consequences of the social isolations [53]. Later, the perceived cost of infection and potential death will become smaller than the felt loss. For instance, college students reported physical exhaustion and decreased motivation among other feelings with more resilience expressed by senior students [57]. An increased adherence to preventive behavior and avoidance of risky behavior is positively associated with age [55]. A continued preventive behavior was found to be related to older ages; however, all ages grew weary of avoiding risky behaviors like meeting non-household members [55]. The needs of work and low socioeconomic status intensified the risky behaviors whereas lower education exacerbated both low adoption of preventive measures and high practice of risky behaviors [55]. Moreover, reports of regional COVID-19 cases and the fear of death increased the likelihood to implement both preventive measures and avoiding risky behaviors [55]. The disease-behavior-economic model presented in this chapter, including many of those aforementioned factors, showed that human behavior through pandemic fatigue can determine the fate of the epidemic as well as the economic growth.

One factor to overcome pandemic fatigue is resilience or the human ability to adapt to the new circumstances and to accept the existence of the disease risk while coping with it. The WHO recommended four strategies for governments to address pandemic fatigue: understanding people, engagement of people, acknowledgment of hardship, and allowing people to live with reduced risk [53].

#### **4.4 Policy reinforcement of social distancing as an influencer**

While most countries around the world implemented early, stringent social distancing policy including lockdowns once the virus began spreading domestically, the Japanese strategy for the COVID-19 outbreak used voluntary guidance for social distancing measures and persuasive messaging. Public health authorities implemented voluntary measures with contact tracing and diagnostic testing. Widely adopted voluntary compliance behaviors appears to have achieved results similar to other countries that used more stringent social interventions (e.g., lockdowns). The policy strategy comes as a trade-off with more healthcare demand and more deaths than if early stringent control was implemented [58]. The strategy's success depends on continued public good will and compliant behaviors. Hofstede cultural dimensions (see **Figure 1**) of high uncertainty avoidance, long-term orientation and masculinity in Japan resulted in high compliance with social isolation. Google mobility data confirms that even in the absence of lockdown the population avoided public transit (e.g., subways, busses, trains), retail stores, and workplaces (see **Figure 3**). The Japanese strategy requires ongoing public health risk communication efforts to maintain high levels of voluntary compliance.

*Human Cultural Dimensions and Behavior during COVID-19 Can Lead to Policy Resistance… DOI: http://dx.doi.org/10.5772/intechopen.96689*

Sweden used no lockdown approach with the public health goals of obtaining herd immunity to COVID-19 (where a threshold is reached where enough of the population would possess immunity to the virus), and secondly as a strategy to minimize economic shock impacts [59]. A similar no lockdown approach was also used in Japan.

In contrast to Japan's voluntary approach, on January 23 2020 China implemented an early mandatory, stringent lockdown strategy in Hubei province affecting 16 cities (including Wuhan) restricting movement of about 57 million people [60]. The unprecedented scale of this lockdown was controversial resulting in an exodus of people out of Wuhan just prior to the lockdown which could have spread the virus. The strategy placed a cordon sanitaire around the city of 11 million people which raised ethical concerns [61]. After 76 days on April 8 2020 Wuhan ended its lockdown [62]. While the Wuhan lockdown was considered a draconian and unprecedented strategy, experts estimated that lockdown in the city of Wuhan prevented between 0.5–3 million infections and 18,000–70,000 deaths at the expense of the economy and in terms of restrictions to personal freedoms [63]. Other countries followed and implemented similar Wuhan-style lockdowns including Italy (provinces of Lombardy and Veneto), Spain, Russia, India and the Philippines [64, 65]. In this way China acted as an "influencer" or role model for other countries that adopted the same type of lockdown, this is an example of reinforcement.

### **4.5 Economy and outcome inelasticity - social intervention failure as an influencer**

Economic downfall due to social interventions including lockdown during COVID-19 have occurred especially in Low- and Middle-Income Countries (LMICs). Other countries like India and Kuwait showed that social interventions failed to effectively reduce local transmission occurring within large migrant laborer populations. The inelasticity occurred with migrant workers in another country (e.g., Indian migrant workers in Kuwait) or workers moving from one state to another state in their home country (e.g., India) [25, 66].

The vast majority of the migrant workers who traveled to Kuwait for work had very limited means. Non-Kuwaiti migrant workers make up more than 60% of the total population and are mostly employed in low-skilled sectors and domestic work. Migrant workers in Kuwait live in cramped dormitories with poor housing conditions having unmaintained and shared toilets, and poor or no ventilation. The lack of social distance and sanitation among occupants resulted in increased COVID-19 transmission among migrant workers [67].

In India, migrant workers usually live and work in megacities under crowded conditions that do not permit social distancing, putting them at an increased risk for disease transmission. Moreover, migrant workers in many LMICs have difficulty gaining access to health care services since they lack health insurance and lack of access to healthcare facilities as a result of administrative barriers [25]. During the COVID-19 pandemic migrant workers from LMICs face conditions that promote inelasticity (communal overcrowded housing, fear of job loss, unsanitary conditions, withheld income and lack of social distancing). Higher GDP countries also encounter this effect but to a much lesser degree with migrant workers (e.g., Canada's Temporary Foreign Worker Program that allows an employer to hire a foreign worker to help harvest crops and fruit) [68]. Many low-income individuals and migrant workers simply cannot adhere to social interventions that reduce transmission risk due to their situation. Their behavioral responses result in unintentional non-compliance and outcome inelasticity.
